A B C D E F G H I K L M N O P R S T U V W Z

A

ABS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns Math.abs(a).

AbstractCrossValidation<M extends IgniteModel<Vector,L>,L,K,V> - Class in org.apache.ignite.ml.selection.cv

Cross validation score calculator.

AbstractCrossValidation() - Constructor for class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

AbstractCrossValidation.TaskResult - Class in org.apache.ignite.ml.selection.cv

Represents the scores and map of parameters.

AbstractLSQR - Class in org.apache.ignite.ml.math.isolve.lsqr

Basic implementation of the LSQR algorithm without assumptions about dataset storage format or data processing device.

AbstractLSQR() - Constructor for class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

AbstractMatrix - Class in org.apache.ignite.ml.math.primitives.matrix

This class provides a helper implementation of the Matrix interface to minimize the effort required to implement it.

AbstractMatrix(MatrixStorage) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

AbstractMatrix() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

AbstractMetrics<M extends MetricValues> - Class in org.apache.ignite.ml.selection.scoring.metric

Abstract metrics calculator.

AbstractMetrics() - Constructor for class org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics

AbstractVector - Class in org.apache.ignite.ml.math.primitives.vector

This class provides a helper implementation of the Vector interface to minimize the effort required to implement it.

AbstractVector(VectorStorage) - Constructor for class org.apache.ignite.ml.math.primitives.vector.AbstractVector

AbstractVector(boolean, VectorStorage) - Constructor for class org.apache.ignite.ml.math.primitives.vector.AbstractVector

AbstractVector() - Constructor for class org.apache.ignite.ml.math.primitives.vector.AbstractVector

accept(A, B, C) - Method in interface org.apache.ignite.ml.math.functions.IgniteTriConsumer

Analogous to 'accept' in Consumer version, but with three parameters.

accessMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

Get access mode.

accessMode() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

accessMode() - Method in interface org.apache.ignite.ml.math.primitives.matrix.OrderedMatrix

Get access mode.

accessMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

accessMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

accessMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

Accuracy<L> - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Accuracy score calculator.

Accuracy() - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy

accuracy() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Accuracy.

activationFunction() - Method in class org.apache.ignite.ml.nn.architecture.TransformationLayerArchitecture

Get activation function for this layer.

Activators - Class in org.apache.ignite.ml.nn

Class containing some common activation functions.

Activators() - Constructor for class org.apache.ignite.ml.nn.Activators

activatorsOutput - Variable in class org.apache.ignite.ml.nn.MLPState

Output of activators.

activatorsOutput(int) - Method in class org.apache.ignite.ml.nn.MLPState

Output of activators of given layer.

AdaptableDatasetModel<I,O,IW,OW,M extends IgniteModel<IW,OW>> - Class in org.apache.ignite.ml.trainers

Model which is composition of form before `andThen` inner Mdl `andThen` after.

AdaptableDatasetModel(IgniteFunction<I, IW>, M, IgniteFunction<OW, O>) - Constructor for class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Construct instance of this class.

AdaptableDatasetTrainer<I,O,IW,OW,M extends IgniteModel<IW,OW>,L> - Class in org.apache.ignite.ml.trainers

Type used to adapt input and output types of wrapped DatasetTrainer.

add(double, M) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

Adds a specific binary classifier to the bunch of same classifiers.

add(MLPArchitecture) - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Creates config describing network where first goes this config and after goes this method's argument.

add(MultilayerPerceptron) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Create MLP where this MLP output is fed as input to added MLP.

add(GiniImpurityMeasure) - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasure

Adds the given impurity to this.

add(T) - Method in interface org.apache.ignite.ml.tree.impurity.ImpurityMeasure

Adds the given impurity to this.

add(MSEImpurityMeasure) - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

Adds the given impurity to this.

add(StepFunction<T>) - Method in class org.apache.ignite.ml.tree.impurity.util.StepFunction

Adds the given step function to this.

add(VectorGenerator, double) - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Add generator to family with weight proportional to it selection probability.

add(VectorGenerator) - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Adds generator to family with weight = 1.

add(Vector, Vector) - Method in class org.apache.ignite.ml.util.generators.standard.GaussianMixtureDataStream.Builder

Adds multidimentional gaussian component.

addElement(T) - Method in interface org.apache.ignite.ml.dataset.feature.Histogram

Add object to histogram.

addElement(T) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Add object to histogram.

addElement(BootstrappedVector) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

Add object to histogram.

addElement(BootstrappedVector) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

Add object to histogram.

addElementToLeafStatistic(ObjectHistogram<BootstrappedVector>, BootstrappedVector, int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.ClassifierLeafValuesComputer

Save vector to leaf statistic.

addElementToLeafStatistic(T, BootstrappedVector, int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

Save vector to leaf statistic.

addElementToLeafStatistic(MeanValueStatistic, BootstrappedVector, int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.RegressionLeafValuesComputer

Save vector to leaf statistic.

addField(String, String) - Method in class org.apache.ignite.ml.util.ModelTrace

Add field.

addField(String, List) - Method in class org.apache.ignite.ml.util.ModelTrace

Add field.

addHyperParam(String, DoubleConsumer, Double[]) - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Adds a grid for the specific hyper parameter.

addMatrix2MatrixTrainer(DatasetTrainer<M1, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Shortcut for adding trainer Matrix -> Matrix where this trainer is treated as Vector -> Vector, where input Vector is turned into 1 x cols Matrix and output is a first row of output Matrix.

addPreprocessingTrainer(PreprocessingTrainer) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Adds a preprocessor.

addTrainer(DatasetTrainer<M1, L>) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Adds submodel trainer along with converters needed on training and inference stages.

addTrainer(DatasetTrainer<M1, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Adds submodel trainer along with converters needed on training and inference stages.

addTrainer(DatasetTrainer<M1, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Adds submodel trainer along with converters needed on training and inference stages.

addTrainer(DatasetTrainer) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Adds a trainer.

addTrainerWithDoubleOutput(DatasetTrainer<M1, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Shortcut for adding trainer Vector -> Double where this trainer is treated as Vector -> Vector, where output Vector is constructed by wrapping double value.

addVectorizer(Vectorizer<K, V, C, L>) - Method in class org.apache.ignite.ml.pipeline.Pipeline

affinityCallWithRetries(Ignite, Collection<String>, IgniteFunction<Integer, R>, int, int, DeployingContext) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Calls the specified fun function on all partitions so that is't guaranteed that partitions with the same index of all specified caches will be placed on the same node and will not be moved before computation is finished.

affinityCallWithRetries(Ignite, Collection<String>, IgniteFunction<Integer, R>, int, DeployingContext) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Calls the specified fun function on all partitions so that is't guaranteed that partitions with the same index of all specified caches will be placed on the same node and will not be moved before computation is finished.

afterFeatureExtractor(IgniteFunction<Vector, Vector>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Specify function which will be applied after feature extractor.

afterLabelExtractor(IgniteFunction<L, L>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Specify function which will be applied after label extractor.

afterTrainedModel(IgniteFunction<O, O1>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Let this trainer produce model mdl.

aggregateImpurityStatistics(ArrayList<TreeRoot>, Map<Integer, BucketMeta>, Map<NodeId, TreeNode>, Dataset<EmptyContext, BootstrappedDatasetPartition>) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer

Computes histograms for each feature.

all() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets iterator over all elements in this vector.

all() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets iterator over all elements in this vector.

all() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets iterator over all elements in this vector.

ALL - Static variable in class org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies

allCoords(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.ArrayLikeVectorizer

Returns list of all coordinate with feature values.

allCoords(K, BinaryObject) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Returns list of all coordinate with feature values.

allCoords(K, LabeledVector<L>) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Returns list of all coordinate with feature values.

allCoords(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Returns list of all coordinate with feature values.

allCoords(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.VectorizerAdapter

Returns list of all coordinate with feature values.

allSpliterator() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets spliterator for all values in this matrix.

allSpliterator() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets spliterator for all values in this matrix.

allSpliterator() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets spliterator for all values in this vector.

allSpliterator() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets spliterator for all values in this vector.

allSpliterator() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets spliterator for all values in this vector.

allUpdatesReducer() - Method in class org.apache.ignite.ml.nn.UpdatesStrategy

Get function used to reduce updates from different trainings (for example, averaging of gradients of all parallel trainings).

amountOfFolds - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Amount of folds.

and(TreeFilter) - Method in interface org.apache.ignite.ml.tree.TreeFilter

Returns a composed predicate.

andBefore(IgniteFunction<V1, T>) - Method in interface org.apache.ignite.ml.IgniteModel

Get a composition model of the form x -> mdl(before(x)).

andBefore(IgniteFunction<I1, I>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Create new AdaptableDatasetModel which is a composition of the form thisMdl . before.

andThen(IgniteFunction<C, C2>) - Method in interface org.apache.ignite.ml.dataset.PartitionContextBuilder

Makes a composed partition context builder that first builds a context and then applies the specified function on the result.

andThen(IgniteBiFunction<D, C, D2>) - Method in interface org.apache.ignite.ml.dataset.PartitionDataBuilder

Makes a composed partition data builder that first builds a data and then applies the specified function on the result.

andThen(UpstreamTransformerBuilder) - Method in interface org.apache.ignite.ml.dataset.UpstreamTransformerBuilder

Combunes two builders (this and other respectfully) env -> transformer1 env -> transformer2 into env -> transformer2 . transformer1

andThen(IgniteModel<V, V1>) - Method in interface org.apache.ignite.ml.IgniteModel

Get a composition model of the form x -> after(mdl(x)).

andThen(IgniteFunction<V, V1>) - Method in interface org.apache.ignite.ml.IgniteModel

Get a composition model of the form x -> after(mdl(x)).

andThen(IgniteFunction<? super R, ? extends V>) - Method in interface org.apache.ignite.ml.math.functions.IgniteBiFunction

andThen(IgniteFunction<? super R, ? extends V>) - Method in interface org.apache.ignite.ml.math.functions.IgniteFunction

Compose this function and given function.

andThen(Function<? super R, ? extends V>) - Method in interface org.apache.ignite.ml.math.functions.IgniteTriFunction

andThen(IgniteModel<O, O1>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Get a composition model of the form x -> after(mdl(x)).

andThen(DatasetTrainer<M1, L>, IgniteFunction<AdaptableDatasetModel<I, O, IW, OW, M>, IgniteFunction<LabeledVector<L>, LabeledVector<L>>>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Create a TrainersSequentialComposition of whis trainer and specified trainer.

ANNClassificationModel - Class in org.apache.ignite.ml.knn.ann

ANN model to predict labels in multi-class classification task.

ANNClassificationModel(LabeledVectorSet<LabeledVector>, ANNClassificationTrainer.CentroidStat) - Constructor for class org.apache.ignite.ml.knn.ann.ANNClassificationModel

Build the model based on a candidates set.

ANNClassificationTrainer - Class in org.apache.ignite.ml.knn.ann

ANN algorithm trainer to solve multi-class classification task.

ANNClassificationTrainer() - Constructor for class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

ANNClassificationTrainer.CentroidStat - Class in org.apache.ignite.ml.knn.ann

Service class used for statistics.

ANNModelFormat - Class in org.apache.ignite.ml.knn.ann

ANN model representation.

ANNModelFormat(int, DistanceMeasure, boolean, LabeledVectorSet<LabeledVector>, ANNClassificationTrainer.CentroidStat) - Constructor for class org.apache.ignite.ml.knn.ann.ANNModelFormat

Creates an instance.

apply(double[]) - Method in class org.apache.ignite.ml.composition.predictionsaggregator.MeanValuePredictionsAggregator

apply(double[]) - Method in class org.apache.ignite.ml.composition.predictionsaggregator.OnMajorityPredictionsAggregator

apply(double[]) - Method in class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

apply(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Extracts labeled vector from upstream object.

apply(int, double) - Method in interface org.apache.ignite.ml.math.functions.IgniteIntDoubleToDoubleBiFunction

apply(int, int) - Method in interface org.apache.ignite.ml.math.functions.IgniteIntIntToIntBiFunction

apply(A, B, C) - Method in interface org.apache.ignite.ml.math.functions.IgniteTriFunction

apply(int, int, double) - Method in interface org.apache.ignite.ml.math.functions.IntIntDoubleToVoidFunction

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.developer.MappedPreprocessor

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.developer.PatchedPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.encoding.onehotencoder.OneHotEncoderPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor

Applies this preprocessor.

apply(K, V) - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerPreprocessor

Applies this preprocessor.

applyGradient(Map<O, Vector>, Map<S, Vector>) - Method in class org.apache.ignite.ml.recommendation.util.MatrixFactorizationGradient

Applies given gradient to recommendation model (object matrix and subject matrix) and updates this model correspondingly.

architecture - Variable in class org.apache.ignite.ml.nn.MultilayerPerceptron

This MLP architecture.

architecture() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get architecture of this MLP.

argmin(List<A>, IgniteFunction<A, B>) - Static method in class org.apache.ignite.ml.math.functions.Functions

ArrayLikeVectorizer(Integer...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.ArrayLikeVectorizer

Creates an instance of Vectorizer.

ArraySpatialIndex<L> - Class in org.apache.ignite.ml.knn.utils.indices

Array based implementation of SpatialIndex.

ArraySpatialIndex(List<LabeledVector<L>>, DistanceMeasure) - Constructor for class org.apache.ignite.ml.knn.utils.indices.ArraySpatialIndex

Construct a new array spatial index.

asArray() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Returns array of doubles corresponds to vector components.

asAscii(Vector, String, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

asAscii(Matrix, String) - Static method in class org.apache.ignite.ml.math.Tracer

asDatasetBuilder(int, int) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Convert first N values from stream to DatasetBuilder.

asDatasetBuilder(int, IgniteBiPredicate<Vector, Double>, int) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Convert first N values from stream to DatasetBuilder.

asDatasetBuilder(int, IgniteBiPredicate<Vector, Double>, int, UpstreamTransformerBuilder) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Convert first N values from stream to DatasetBuilder.

asDataStream() - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Conterts vectors generator to unlabeled data stream generator.

asDataStream() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily

Creates data stream where label of vector == id of distribution from family.

asDense(SparseMatrix, int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

asFeatureExtractor(FeatureLabelExtractor<K, V, L>) - Static method in class org.apache.ignite.ml.composition.CompositionUtils

Create feature extractor from given mapping (key, value) -> LabeledVector.

asLabelExtractor(FeatureLabelExtractor<K, V, L>) - Static method in class org.apache.ignite.ml.composition.CompositionUtils

Label extractor feature extractor from given mapping (key, value) -> LabeledVector.

asLIBSVM(String, String, String, Random, int) - Static method in class org.apache.ignite.ml.util.MnistUtils

Convert random count samples from MNIST dataset from two files (images and labels) into libsvm format.

asMap(int) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Convert first N values from stream to map.

assertAccessMode(int) - Method in interface org.apache.ignite.ml.math.StorageConstants

assertStorageMode(int) - Method in interface org.apache.ignite.ml.math.StorageConstants

assign(double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns given value to all elements of this matrix.

assign(IntIntToDoubleFunction) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns each matrix element to the value generated by given function.

assign(double[][]) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns given values to this matrix.

assign(Matrix) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns values from given matrix to this matrix.

assign(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns given value to all elements of this matrix.

assign(double[][]) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns given values to this matrix.

assign(Matrix) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns values from given matrix to this matrix.

assign(IntIntToDoubleFunction) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns each matrix element to the value generated by given function.

assign(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Assigns given value to all elements of this vector.

assign(double[]) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Assigns values from given array to this vector.

assign(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Copies values from the argument vector to this one.

assign(IntToDoubleFunction) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Assigns each vector element to the value generated by given function.

assign(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Assigns given value to all elements of this vector.

assign(double[]) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Assigns values from given array to this vector.

assign(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Copies values from the argument vector to this one.

assign(IntToDoubleFunction) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Assigns each vector element to the value generated by given function.

assign(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Assigns given value to all elements of this vector.

assign(double[]) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Assigns values from given array to this vector.

assign(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Copies values from the argument vector to this one.

assign(IntToDoubleFunction) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Assigns each vector element to the value generated by given function.

assignColumn(int, Vector) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns values from given vector to the specified column in this matrix.

assignColumn(int, Vector) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns values from given vector to the specified column in this matrix.

assignPartitions(AffinityFunctionContext) - Method in class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

assignRow(int, Vector) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Assigns values from given vector to the specified row in this matrix.

assignRow(int, Vector) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Assigns values from given vector to the specified row in this matrix.

asStream(Iterator<T>, long) - Static method in class org.apache.ignite.ml.util.Utils

Convert given iterator to a stream with known count of entries.

asStream(Iterator<T>) - Static method in class org.apache.ignite.ml.util.Utils

Convert given iterator to a stream.

AsyncModelBuilder - Interface in org.apache.ignite.ml.inference.builder

Builder of asynchronous inference model.

avg(List<NesterovParameterUpdate>) - Static method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Get average of parameters updates.

AVG - Static variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Averages updates returned by different trainings.

AVG - Static variable in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate

Method used to sum updates inside of one of parallel trainings.

axpy(Double, Vector, Vector) - Static method in class org.apache.ignite.ml.math.Blas

Performs y += a * x

B

BaggedModel - Class in org.apache.ignite.ml.composition.bagging

This class represents model produced by BaggedTrainer.

BaggedTrainer<L> - Class in org.apache.ignite.ml.composition.bagging

Trainer encapsulating logic of bootstrap aggregating (bagging).

BaggedTrainer(DatasetTrainer<? extends IgniteModel, L>, PredictionsAggregator, int, double, int, int) - Constructor for class org.apache.ignite.ml.composition.bagging.BaggedTrainer

Construct instance of this class with given parameters.

BaggingUpstreamTransformer - Class in org.apache.ignite.ml.trainers.transformers

This class encapsulates the logic needed to do bagging (bootstrap aggregating) by features.

BaggingUpstreamTransformer(long, double) - Constructor for class org.apache.ignite.ml.trainers.transformers.BaggingUpstreamTransformer

Construct instance of this transformer with a given subsample ratio.

balancedAccuracy() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Balanced accuracy.

BallTreeSpatialIndex<L> - Class in org.apache.ignite.ml.knn.utils.indices

Ball tree based implementation of SpatialIndex.

BallTreeSpatialIndex(List<LabeledVector<L>>, DistanceMeasure) - Constructor for class org.apache.ignite.ml.knn.utils.indices.BallTreeSpatialIndex

Constructs a new instance of Ball tree spatial index.

baseMdlTrainerBuilder - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Base model trainer builder.

basePreprocessor - Variable in class org.apache.ignite.ml.preprocessing.encoding.EncoderPreprocessor

Base preprocessor.

beforeTrainedModel(IgniteFunction<I1, I>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Let this trainer produce model mdl.

below - Variable in class org.apache.ignite.ml.nn.MultilayerPerceptron

MLP which is 'below' this MLP (i.e. below output goes to this MLP as input).

belowLayersCount() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Count of layers in below MLP.

beta(double[], double, double) - Method in class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

Calculates beta.

beta(double[], double, double) - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

Calculates beta.

bias(int, int) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get the bias of given neuron in given layer.

biases - Variable in class org.apache.ignite.ml.nn.MLPLayer

Biases vector.

biases(int) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get biases of layer with given index.

BinarizationPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.binarization

Preprocessing function that makes binarization.

BinarizationPreprocessor(double, Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.binarization.BinarizationPreprocessor

Constructs a new instance of Binarization preprocessor.

BinarizationTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.binarization

Trainer of the binarization preprocessor.

BinarizationTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.binarization.BinarizationTrainer

BinaryClassificationMetrics - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Binary classification metrics calculator.

BinaryClassificationMetrics() - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

BinaryClassificationMetricValues - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Provides access to binary metric values.

BinaryClassificationMetricValues(long, long, long, long, double) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Initialize an example by 4 metrics.

BinaryObjectVectorizer<K> - Class in org.apache.ignite.ml.dataset.feature.extractor.impl

Vectorizer on binary objects.

BinaryObjectVectorizer(String...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Creates an instance of Vectorizer.

BinaryObjectVectorizer.Mapping - Class in org.apache.ignite.ml.dataset.feature.extractor.impl

Feature values mapping for non-number features.

Blas - Class in org.apache.ignite.ml.math

Useful subset of BLAS operations.

Blas() - Constructor for class org.apache.ignite.ml.math.Blas

blur(RandomProducer) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Apply pseudorandom noize to vectors without labels mapping.

bnorm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

Calculates bnorm.

bnorm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

Calculates bnorm.

BootstrappedDatasetBuilder<K,V> - Class in org.apache.ignite.ml.dataset.impl.bootstrapping

Builder for bootstrapped dataset.

BootstrappedDatasetBuilder(Preprocessor<K, V>, int, double) - Constructor for class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetBuilder

Creates an instance of BootstrappedDatasetBuilder.

BootstrappedDatasetPartition - Class in org.apache.ignite.ml.dataset.impl.bootstrapping

Partition of bootstrapped vectors.

BootstrappedDatasetPartition(BootstrappedVector[]) - Constructor for class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

Creates an instance of BootstrappedDatasetPartition.

BootstrappedVector - Class in org.apache.ignite.ml.dataset.impl.bootstrapping

Represents vector with repetitions counters for subsamples in bootstrapped dataset.

BootstrappedVector(Vector, double, int[]) - Constructor for class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

Creates an instance of BootstrappedVector.

BootstrappedVectorsHistogram - Class in org.apache.ignite.ml.tree.randomforest.data.impurity.basic

Histogram for bootstrapped vectors with predefined bucket mapping logic for feature id == featureId.

BootstrappedVectorsHistogram(Set<Integer>, BucketMeta, int) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.BootstrappedVectorsHistogram

Creates an instance of BootstrappedVectorsHistogram.

BruteForceStrategy - Class in org.apache.ignite.ml.selection.paramgrid

This strategy enables the brute-force search in hyper-parameter space.

BruteForceStrategy() - Constructor for class org.apache.ignite.ml.selection.paramgrid.BruteForceStrategy

bucketIds - Variable in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.BootstrappedVectorsHistogram

Bucket ids.

bucketIds - Variable in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogram

Bucket ids.

bucketIdToValue(int) - Method in class org.apache.ignite.ml.dataset.feature.BucketMeta

Returns mean value by bucket id.

BucketMeta - Class in org.apache.ignite.ml.dataset.feature

Bucket meta-information for feature histogram.

BucketMeta(FeatureMeta) - Constructor for class org.apache.ignite.ml.dataset.feature.BucketMeta

Creates an instance of BucketMeta.

bucketMeta - Variable in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.BootstrappedVectorsHistogram

Bucket meta.

buckets() - Method in interface org.apache.ignite.ml.dataset.feature.Histogram

buckets() - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

buckets() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

buckets() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

build(LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Method in interface org.apache.ignite.ml.dataset.DatasetBuilder

Constructs a new instance of Dataset that includes allocation required data structures and initialization of context part of partitions.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, EmptyContext) - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Constructs a new instance of Dataset that includes allocation required data structures and initialization of context part of partitions.

build(LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Constructs a new instance of Dataset that includes allocation required data structures and initialization of context part of partitions.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long) - Method in interface org.apache.ignite.ml.dataset.PartitionContextBuilder

Builds a new partition context from an upstream data.

build(LearningEnvironment, Stream<UpstreamEntry<K, V>>, long) - Method in interface org.apache.ignite.ml.dataset.PartitionContextBuilder

Builds a new partition context from an upstream data.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in interface org.apache.ignite.ml.dataset.PartitionDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Stream<UpstreamEntry<K, V>>, long, C) - Method in interface org.apache.ignite.ml.dataset.PartitionDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long) - Method in class org.apache.ignite.ml.dataset.primitive.builder.context.EmptyContextBuilder

Builds a new partition context from an upstream data.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.dataset.primitive.builder.data.SimpleDatasetDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.dataset.primitive.builder.data.SimpleLabeledDatasetDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment) - Method in interface org.apache.ignite.ml.dataset.UpstreamTransformerBuilder

Create UpstreamTransformer based on learning environment.

build(ModelReader, ModelParser<I, O, ?>) - Method in interface org.apache.ignite.ml.inference.builder.AsyncModelBuilder

Builds asynchronous inference model using specified model reader and model parser.

build(ModelReader, ModelParser<I, O, ?>) - Method in class org.apache.ignite.ml.inference.builder.IgniteDistributedModelBuilder

Starts the specified in constructor number of service instances and request/response queues.

build(ModelReader, ModelParser<I, O, M>) - Method in class org.apache.ignite.ml.inference.builder.SingleModelBuilder

Builds synchronous inference model using specified model reader and model parser.

build(ModelReader, ModelParser<I, O, M>) - Method in interface org.apache.ignite.ml.inference.builder.SyncModelBuilder

Builds synchronous inference model using specified model reader and model parser.

build(ModelReader, ModelParser<I, O, ?>) - Method in class org.apache.ignite.ml.inference.builder.ThreadedModelBuilder

Builds asynchronous inference model using specified model reader and model parser.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, EmptyContext) - Method in class org.apache.ignite.ml.knn.KNNPartitionDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<Object, BinaryObject>>, long, EmptyContext) - Method in class org.apache.ignite.ml.recommendation.util.RecommendationBinaryDatasetDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, Z>>, long, EmptyContext) - Method in class org.apache.ignite.ml.recommendation.util.RecommendationDatasetDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.structures.partition.LabeledDatasetPartitionDataBuilderOnHeap

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.structures.partition.LabelPartitionDataBuilderOnHeap

Builds a new partition data from a partition upstream data and partition context.

build(LearningEnvironment, Iterator<UpstreamEntry<K, V>>, long, C) - Method in class org.apache.ignite.ml.tree.data.DecisionTreeDataBuilder

Builds a new partition data from a partition upstream data and partition context.

build() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Builds VectorGeneratorsFamily instance.

build(long) - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Builds VectorGeneratorsFamily instance.

build() - Method in class org.apache.ignite.ml.util.generators.standard.GaussianMixtureDataStream.Builder

build(long) - Method in class org.apache.ignite.ml.util.generators.standard.GaussianMixtureDataStream.Builder

buildBaseModelTrainer() - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Returns regressor model trainer for one step of GDB.

buildBaseModelTrainer() - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Returns regressor model trainer for one step of GDB.

buildBaseModelTrainer() - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Returns regressor model trainer for one step of GDB.

buildComposition(List<TreeRoot>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Returns composition of built trees.

buildComposition(List<TreeRoot>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer

Returns composition of built trees.

buildComposition(List<TreeRoot>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Returns composition of built trees.

buildDataset(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.knn.utils.KNNUtils

Builds dataset.

builder(double, int) - Static method in class org.apache.ignite.ml.trainers.transformers.BaggingUpstreamTransformer

Get builder of BaggingUpstreamTransformer for a model with a specified index in ensemble.

Builder() - Constructor for class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Builder() - Constructor for class org.apache.ignite.ml.util.generators.standard.GaussianMixtureDataStream.Builder

builder(String, boolean) - Static method in class org.apache.ignite.ml.util.ModelTrace

Creates an instance of ModelTrace.

builder(String) - Static method in class org.apache.ignite.ml.util.ModelTrace

Creates an instance of ModelTrace.

buildForTrainer() - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Builds learning environment for trainer.

buildForWorker(int) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Builds LearningEnvironment for worker on given partition.

buildForWorker(int) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Builds LearningEnvironment for worker on given partition.

buildLabeledDatasetOnListOfVectors(List<LabeledVector>) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

C

CacheBasedDataset<K,V,C extends Serializable,D extends AutoCloseable> - Class in org.apache.ignite.ml.dataset.impl.cache

An implementation of dataset based on Ignite Cache, which is used as upstream and as reliable storage for partition context as well.

CacheBasedDataset(Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>, UpstreamTransformerBuilder, IgniteCache<Integer, C>, LearningEnvironmentBuilder, PartitionDataBuilder<K, V, C, D>, UUID, boolean, LearningEnvironment, int) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

Constructs a new instance of dataset based on Ignite Cache, which is used as upstream and as reliable storage for partition context as well.

CacheBasedDatasetBuilder<K,V> - Class in org.apache.ignite.ml.dataset.impl.cache

A dataset builder that makes CacheBasedDataset.

CacheBasedDatasetBuilder(Ignite, IgniteCache<K, V>) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset with default predicate that passes all upstream entries to dataset.

CacheBasedDatasetBuilder(Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset.

CacheBasedDatasetBuilder(Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>, UpstreamTransformerBuilder) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset.

CacheBasedDatasetBuilder(Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>, UpstreamTransformerBuilder, Boolean, int) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset.

CacheBasedLabelPairCursor<L,K,V> - Class in org.apache.ignite.ml.selection.scoring.cursor

Truth with prediction cursor based on a data stored in Ignite cache.

CacheBasedLabelPairCursor(IgniteCache<K, V>, IgniteBiPredicate<K, V>, Preprocessor<K, V>, IgniteModel<Vector, L>) - Constructor for class org.apache.ignite.ml.selection.scoring.cursor.CacheBasedLabelPairCursor

Constructs a new instance of cache based truth with prediction cursor.

CacheBasedLabelPairCursor(IgniteCache<K, V>, Preprocessor<K, V>, IgniteModel<Vector, L>) - Constructor for class org.apache.ignite.ml.selection.scoring.cursor.CacheBasedLabelPairCursor

Constructs a new instance of cache based truth with prediction cursor.

calculate(DecisionTreeData, TreeFilter, int) - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasureCalculator

Calculates all impurity measures required required to find a best split and returns them as an array of StepFunction (for every column).

calculate(DecisionTreeData, TreeFilter, int) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Calculates all impurity measures required required to find a best split and returns them as an array of StepFunction (for every column).

calculate(DecisionTreeData, TreeFilter, int) - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasureCalculator

Calculates all impurity measures required required to find a best split and returns them as an array of StepFunction (for every column).

calculateFitnessForAll(Function<Chromosome, Double>) - Method in class org.apache.ignite.ml.util.genetic.Population

Calculates fintness for all chromosomes with custom fitness function.

calculateFitnessForChromosome(int, Function<Chromosome, Double>) - Method in class org.apache.ignite.ml.util.genetic.Population

Calculates fitness for chromosome found by index with custom fitness function.

calculateGradient(Map<O, Vector>, Map<S, Vector>, int, int, double, double) - Method in class org.apache.ignite.ml.recommendation.util.RecommendationDatasetData

Calculates gradient of the loss function of recommendation system SGD training.

calculateNewUpdate(M, NesterovParameterUpdate, int, Matrix, Matrix) - Method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovUpdateCalculator

Calculate new update.

calculateNewUpdate(M, P, int, Matrix, Matrix) - Method in interface org.apache.ignite.ml.optimization.updatecalculators.ParameterUpdateCalculator

Calculate new update.

calculateNewUpdate(SmoothParametrized, RPropParameterUpdate, int, Matrix, Matrix) - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Calculate new update.

calculateNewUpdate(SmoothParametrized, SimpleGDParameterUpdate, int, Matrix, Matrix) - Method in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Calculate new update.

calculateROCAUC(PriorityQueue<Pair<Double, Double>>, long, long, double) - Static method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Calculates the ROC AUC value based on queue of pairs, amount of positive/negative cases and label of positive class.

call(long, byte, BinaryRawReader) - Method in class org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor

cancel(boolean) - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

cancel(boolean) - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

CardinalityException - Exception in org.apache.ignite.ml.math.exceptions

Indicates a cardinality mismatch in matrix or vector operations.

CardinalityException(int, int) - Constructor for exception org.apache.ignite.ml.math.exceptions.CardinalityException

Creates new cardinality violation exception.

categoryFrequencies() - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderPartitionData

Gets the array of maps of frequencies by value in partition for each feature in the dataset.

CentroidStat() - Constructor for class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer.CentroidStat

checkAndReturnSplitValue(int, double, double, double) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogram

Checks split value validity and return Optional-wrap of it.

checkCardinality(Matrix, Vector) - Static method in class org.apache.ignite.ml.math.Blas

Checks if Matrix A can be multiplied by vector v, if not CardinalityException is thrown.

checkCardinality(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

checkCardinality(double[]) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

checkCardinality(int[]) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

checkConvergenceStgyFactory - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Check convergence strategy factory.

checkConvergenceStgyFactory - Variable in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Check convergence strategy factory.

checkIndex(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Check index bounds.

Chromosome - Class in org.apache.ignite.ml.genetic

Represents a potential solution consisting of a fixed-length collection of genes.

Chromosome(long[]) - Constructor for class org.apache.ignite.ml.genetic.Chromosome

Chromosome - Class in org.apache.ignite.ml.util.genetic

Representes the set of genes, known as chromosome in genetic programming.

Chromosome(int) - Constructor for class org.apache.ignite.ml.util.genetic.Chromosome

Chromosome(Double[]) - Constructor for class org.apache.ignite.ml.util.genetic.Chromosome

ChromosomeCriteria - Class in org.apache.ignite.ml.genetic.parameter

Responsible for describing the characteristics of an individual Chromosome.

ChromosomeCriteria() - Constructor for class org.apache.ignite.ml.genetic.parameter.ChromosomeCriteria

circle(double) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of 2D-vectors from circle-like distribution around zero.

circle(double, long) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of 2D-vectors from circle-like distribution around zero.

ClassifierLeafValuesComputer - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Implementation of LeafValuesComputer for classification task.

ClassifierLeafValuesComputer(Map<Double, Integer>) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.ClassifierLeafValuesComputer

Creates an instance of ClassifierLeafValuesComputer.

ClassMetric<L> - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Metric calculator for one class label.

ClassMetric(L) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.ClassMetric

The class of interest or positive class.

clientClassLoader() - Method in interface org.apache.ignite.ml.environment.deploy.DeployingContext

clientClassLoader() - Method in class org.apache.ignite.ml.environment.deploy.DeployingContextImpl

close() - Method in class org.apache.ignite.ml.composition.bagging.BaggedModel

close() - Method in class org.apache.ignite.ml.composition.combinators.parallel.ModelsParallelComposition

close() - Method in class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

close() - Method in class org.apache.ignite.ml.composition.stacking.StackedModel

close() - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

close() - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

close() - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDataset

close() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleDatasetData

close() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData

close() - Method in class org.apache.ignite.ml.dataset.primitive.DatasetWrapper

close() - Method in class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData

close() - Method in interface org.apache.ignite.ml.IgniteModel

close() - Method in interface org.apache.ignite.ml.inference.Model

close() - Method in class org.apache.ignite.ml.knn.KNNModel

close() - Method in interface org.apache.ignite.ml.knn.utils.indices.SpatialIndex

close() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

close() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Destroys decomposition components and other internal components of decomposition.

close() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesSumsHolder

close() - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderPartitionData

close() - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

close() - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPartitionData

close() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPartitionData

close() - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerData

close() - Method in class org.apache.ignite.ml.recommendation.util.RecommendationDatasetData

close() - Method in class org.apache.ignite.ml.selection.scoring.cursor.CacheBasedLabelPairCursor

close() - Method in class org.apache.ignite.ml.selection.scoring.cursor.LocalLabelPairCursor

close() - Method in class org.apache.ignite.ml.structures.LabeledVectorSet

Closes LabeledDataset.

close() - Method in class org.apache.ignite.ml.structures.partition.LabelPartitionDataOnHeap

close() - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

clsLb - Variable in class org.apache.ignite.ml.selection.scoring.metric.classification.ClassMetric

Class label.

cluster(P, int) - Method in interface org.apache.ignite.ml.clustering.kmeans.Clusterer

Cluster given points set into k clusters.

Clusterer<P,M extends IgniteModel> - Interface in org.apache.ignite.ml.clustering.kmeans

Base interface for clusterers.

ClusterizationModel<P,V> - Interface in org.apache.ignite.ml.clustering.kmeans

Base interface for all clusterization models.

cntOfIterations - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Count of iterations.

colSize - Variable in class org.apache.ignite.ml.structures.Dataset

Amount of attributes in each vector.

colSize() - Method in class org.apache.ignite.ml.structures.Dataset

Gets amount of attributes.

column() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix.Element

Gets element's column index.

COLUMN_STORAGE_MODE - Static variable in interface org.apache.ignite.ml.math.StorageConstants

Storage mode optimized for column access.

ColumnIndexException - Exception in org.apache.ignite.ml.math.exceptions

This exception is used to indicate any error condition accessing matrix elements by invalid column index.

ColumnIndexException(int) - Constructor for exception org.apache.ignite.ml.math.exceptions.ColumnIndexException

columnOffset() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

columnsCount() - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

columnsCount(DecisionTreeData, TreeDataIndex) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Returns columns count in current dataset.

columnSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets number of columns in this matrix.

columnSize() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets number of columns in this matrix.

columnSize() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

columnSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

columnSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

columnSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

columnsLength() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

combine(IgniteModel<T, W>, BiFunction<V, W, X>) - Method in interface org.apache.ignite.ml.IgniteModel

Combines this model with other model via specified combiner

COMPARE - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns {@code a < b ?

compareTo(PointWithDistance) - Method in class org.apache.ignite.ml.knn.utils.PointWithDistance

compareTo(T) - Method in interface org.apache.ignite.ml.tree.impurity.ImpurityMeasure

compareTo(Object) - Method in class org.apache.ignite.ml.util.genetic.Chromosome

componentsProbs() - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

CompositionUtils - Class in org.apache.ignite.ml.composition

Various utility functions for trainers composition.

CompositionUtils() - Constructor for class org.apache.ignite.ml.composition.CompositionUtils

compositionWeights - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Composition weights.

compress(StepFunction<T>) - Method in class org.apache.ignite.ml.tree.impurity.util.SimpleStepFunctionCompressor

Compresses the given step function.

compress(StepFunction<T>) - Method in interface org.apache.ignite.ml.tree.impurity.util.StepFunctionCompressor

Compresses the given step function.

compress(StepFunction<T>[]) - Method in interface org.apache.ignite.ml.tree.impurity.util.StepFunctionCompressor

Compresses every step function in the given array.

compute(IgniteBiFunction<D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteBiFunction<D, LearningEnvironment, R>, IgniteBinaryOperator<R>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteFunction<D, R>, IgniteBinaryOperator<R>, R) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteFunction<D, R>, IgniteBinaryOperator<R>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteBiConsumer<D, LearningEnvironment>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data in the dataset and LearningEnvironment.

compute(IgniteConsumer<D>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data in the dataset.

compute(IgniteBiFunction<D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

Applies the specified map function to every partition data and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteBiFunction<D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDataset

Applies the specified map function to every partition data and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

compute(IgniteBiFunction<D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.primitive.DatasetWrapper

Applies the specified map function to every partition data and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

compute(Vector, Vector) - Method in interface org.apache.ignite.ml.math.distances.DistanceMeasure

Compute the distance between two n-dimensional vectors.

compute(Vector, double[]) - Method in interface org.apache.ignite.ml.math.distances.DistanceMeasure

Compute the distance between n-dimensional vector and n-dimensional array.

compute(Vector, Vector) - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

Compute the distance between two n-dimensional vectors.

compute(Vector, double[]) - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

Compute the distance between n-dimensional vector and n-dimensional array.

compute(Vector, Vector) - Method in class org.apache.ignite.ml.math.distances.HammingDistance

Compute the distance between two n-dimensional vectors.

compute(Vector, double[]) - Method in class org.apache.ignite.ml.math.distances.HammingDistance

Compute the distance between n-dimensional vector and n-dimensional array.

compute(Vector, Vector) - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

Compute the distance between two n-dimensional vectors.

compute(Vector, double[]) - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

Compute the distance between n-dimensional vector and n-dimensional array.

compute(int, int, IgniteTriFunction<Integer, Integer, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Replace matrix entry with value oldVal at (row, col) with result of computing f(row, col, oldVal).

compute(int, int, IgniteTriFunction<Integer, Integer, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Replace matrix entry with value oldVal at (row, col) with result of computing f(row, col, oldVal).

compute(int, int, IgniteTriFunction<Integer, Integer, Double, Double>) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Replace matrix entry with value oldVal at (row, col) with result of computing f(row, col, oldVal).

compute(int, int, IgniteTriFunction<Integer, Integer, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

compute(int, IgniteIntDoubleToDoubleBiFunction) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Replace vector entry with value oldVal at i with result of computing f(i, oldVal).

compute(int, IgniteIntDoubleToDoubleBiFunction) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Replace vector entry with value oldVal at i with result of computing f(i, oldVal).

compute(int, IgniteIntDoubleToDoubleBiFunction) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Replace vector entry with value oldVal at i with result of computing f(i, oldVal).

computeDistributionFunction() - Method in interface org.apache.ignite.ml.dataset.feature.DistributionComputer

Compute distribution function.

computeDistributionFunction() - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Compute distribution function.

computeError(Vector, Double, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

Compute error for the specific vector of dataset.

computeInitialValue(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Compute mean value of label as first approximation.

computeLeafValue(ObjectHistogram<BootstrappedVector>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.ClassifierLeafValuesComputer

Returns the most frequent value in according to statistic.

computeLeafValue(T) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

Compute value from leaf based on statistics on labels corresponds to leaf.

computeLeafValue(MeanValueStatistic) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.RegressionLeafValuesComputer

Returns the mean value in according to statistic.

computeMeanErrorOnDataset(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

Compute error for given model on learning dataset.

computeMeanErrorOnDataset(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceChecker

Compute error for given model on learning dataset.

computeMeanErrorOnDataset(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceChecker

Compute error for given model on learning dataset.

computeMeanErrorOnDataset(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub

Compute error for given model on learning dataset.

computeState(Matrix) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Perform forward pass and return state of outputs of each layer.

computeStatistics(List<FeatureMeta>, Dataset<EmptyContext, BootstrappedDatasetPartition>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatisticsComputer

Computes statistics of normal distribution on features in dataset.

computeStatsOnPartition(BootstrappedDatasetPartition, List<FeatureMeta>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatisticsComputer

Aggregates normal distribution statistics for continual features in dataset partition.

ComputeUtils - Class in org.apache.ignite.ml.dataset.impl.cache.util

Util class that provides common methods to perform computations on top of the Ignite Compute Grid.

ComputeUtils() - Constructor for class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data, context and partition index in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R>, IgniteBinaryOperator<R>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data, context and LearningEnvironment in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteBiFunction<C, D, R>, IgniteBinaryOperator<R>, R) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data and context in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteBiFunction<C, D, R>, IgniteBinaryOperator<R>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data and context in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteTriConsumer<C, D, LearningEnvironment>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data, context and LearningEnvironment in the dataset.

computeWithCtx(IgniteBiConsumer<C, D>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Applies the specified map function to every partition data and context in the dataset.

computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

Applies the specified map function to every partition data, context and partition index in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDataset

Applies the specified map function to every partition data, context and partition index in the dataset and then reduces map results to final result by using the reduce function.

computeWithCtx(IgniteTriFunction<C, D, LearningEnvironment, R>, IgniteBinaryOperator<R>, R) - Method in class org.apache.ignite.ml.dataset.primitive.DatasetWrapper

Applies the specified map function to every partition data, context and partition index in the dataset and then reduces map results to final result by using the reduce function.

concat(Vector, Vector) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Concatenates two given vectors.

concat(Vector, Vector...) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Concatenates given vectors.

concat(Vector...) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Concatenates given vectors.

concat(VectorGenerator) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Creates new generator by concatenation of vectors of this generator and other.

concat(RandomProducer) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Creates new generator by concatenation of vectors of this generator and random producer.

ConsoleLogger - Class in org.apache.ignite.ml.environment.logging

Simple logger printing to STD-out.

ConsoleLogger.Factory - Class in org.apache.ignite.ml.environment.logging

ConsoleLogger factory.

constant(Double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns passed constant.

constant(R) - Static method in interface org.apache.ignite.ml.math.functions.IgniteFunction

IgniteFunction returning specified constant.

constant(Vector) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

ConvergenceChecker<K,V> - Class in org.apache.ignite.ml.composition.boosting.convergence

Contains logic of error computing and convergence checking for Gradient Boosting algorithms.

ConvergenceChecker(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>, double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

Constructs an instance of ConvergenceChecker.

ConvergenceCheckerFactory - Class in org.apache.ignite.ml.composition.boosting.convergence

Factory for ConvergenceChecker.

ConvergenceCheckerFactory(double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceCheckerFactory

Creates an instance of ConvergenceCheckerFactory.

ConvergenceCheckerStub<K,V> - Class in org.apache.ignite.ml.composition.boosting.convergence.simple

This strategy skip estimating error on dataset step.

ConvergenceCheckerStub(long, IgniteFunction, Loss, DatasetBuilder, Preprocessor<K, V>, double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub

Creates an instance of ConvergenceCheckerStub.

ConvergenceCheckerStubFactory - Class in org.apache.ignite.ml.composition.boosting.convergence.simple

Factory for ConvergenceCheckerStub.

ConvergenceCheckerStubFactory() - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStubFactory

Create an instance of ConvergenceCheckerStubFactory.

convertDatasetIntoModel(Dataset<EmptyContext, SpatialIndex<Double>>) - Method in class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer

Convers given dataset into KNN model (classification or regression depends on implementation).

convertDatasetIntoModel(Dataset<EmptyContext, SpatialIndex<Double>>) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Convers given dataset into KNN model (classification or regression depends on implementation).

convertDatasetIntoModel(Dataset<EmptyContext, SpatialIndex<Double>>) - Method in class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer

Convers given dataset into KNN model (classification or regression depends on implementation).

convertStringNamesToFeatureMetadata(String[]) - Method in class org.apache.ignite.ml.structures.Dataset

copy(Vector, Vector) - Method in class org.apache.ignite.ml.math.Blas

Copies Vector x into Vector y.

copy() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Clones this matrix.

copy() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

Clones this matrix.

copy() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Clones this matrix.

copy() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

Clones this matrix.

copy() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Clones this matrix.

copy() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new copy of this vector.

copy() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new copy of this vector.

copy() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

Creates new copy of this vector.

copy() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

Creates new copy of this vector.

copy() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new copy of this vector.

copy(Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the copy of matrix with read-only matrices support.

copy() - Method in class org.apache.ignite.ml.structures.LabeledVectorSet

Makes copy with new Label objects and old features and Metadata objects.

copy() - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Creates chromosome copy.

copy(T) - Static method in class org.apache.ignite.ml.util.Utils

Perform deep copy of an object.

copyOfRange(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Copies the specified range of the vector into a new vector.

copyOfRange(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Copies the specified range of the vector into a new vector.

copyOfRange(int, int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Copies the specified range of the vector into a new vector.

copyParametersFrom(NNClassificationModel) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Sets parameters from other model to this model.

copyPart(Vector, int, int) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Get copy of part of given length of given vector starting from given offset.

copyright() - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

corr() - Method in class org.apache.ignite.ml.dataset.primitive.SimpleDataset

Calculates correlation matrix by all columns.

counters() - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

CountersHistogram - Class in org.apache.ignite.ml.tree.randomforest.data.impurity.basic

Represents a historam of element counts per bucket.

CountersHistogram(Set<Integer>, BucketMeta, int, int) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.CountersHistogram

Creates an instance of CountersHistogram.

countOfComponents() - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

counts() - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Gets the array of amounts of values in partition for each feature in the dataset.

cov() - Method in class org.apache.ignite.ml.dataset.primitive.SimpleDataset

Calculates covariance matrix by all columns.

covariance() - Method in class org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution

CovarianceMatricesAggregator - Class in org.apache.ignite.ml.clustering.gmm

This class encapsulates statistics aggregation logic for feature vector covariance matrix computation of one GMM component (cluster).

create(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceCheckerFactory

Create an instance of ConvergenceChecker.

create(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerFactory

Create an instance of ConvergenceChecker.

create(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerFactory

Create an instance of ConvergenceChecker.

create(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStubFactory

Create an instance of ConvergenceChecker.

create(DatasetBuilder<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed dataset using the specified partCtxBuilder and partDataBuilder.

create(DatasetBuilder<K, V>, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed dataset using the specified partCtxBuilder and partDataBuilder.

create(Ignite, IgniteCache<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed dataset using the specified partCtxBuilder and partDataBuilder.

create(Ignite, IgniteCache<K, V>, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed dataset using the specified partCtxBuilder and partDataBuilder.

create(Map<K, V>, LearningEnvironmentBuilder, int, PartitionContextBuilder<K, V, C>, PartitionDataBuilder<K, V, C, D>, LearningEnvironment) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of local dataset using the specified partCtxBuilder and partDataBuilder.

create() - Static method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer.Mapping

Creates an instance of Mapping.

create(Class<T>) - Method in class org.apache.ignite.ml.environment.logging.ConsoleLogger.Factory

Creates an instance of MLLogger for target class.

create(Class<T>) - Method in interface org.apache.ignite.ml.environment.logging.MLLogger.Factory

Creates an instance of MLLogger for target class.

createCacheProvider(CachePluginContext) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

createComponent(PluginContext, Class<T>) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

createImpurityComputerForFeature(int, BucketMeta) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogramsComputer

Creates impurity computer in according to specific algorithm based on random forest (for example GiniHistogram for classification).

createImpurityComputerForFeature(int, BucketMeta) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer

Creates impurity computer in according to specific algorithm based on random forest (for example GiniHistogram for classification).

createImpurityComputerForFeature(int, BucketMeta) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogramComputer

Creates impurity computer in according to specific algorithm based on random forest (for example GiniHistogram for classification).

createImpurityHistogramsComputer() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Creates an instance of Histograms Computer corresponding to RF implementation.

createImpurityHistogramsComputer() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer

Creates an instance of Histograms Computer corresponding to RF implementation.

createImpurityHistogramsComputer() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Creates an instance of Histograms Computer corresponding to RF implementation.

createIndexByFilter(int, TreeFilter) - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

Builds index in according to current tree depth and cached indexes in upper levels.

createLeaf(TreeNode) - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

Convert node to leaf.

createLeafNode(Dataset<EmptyContext, DecisionTreeData>, TreeFilter) - Method in interface org.apache.ignite.ml.tree.leaf.DecisionTreeLeafBuilder

Creates new leaf node for given dataset and node predicate.

createLeafNode(Dataset<EmptyContext, DecisionTreeData>, TreeFilter) - Method in class org.apache.ignite.ml.tree.leaf.MeanDecisionTreeLeafBuilder

Creates new leaf node for given dataset and node predicate.

createLeafNode(Dataset<EmptyContext, DecisionTreeData>, TreeFilter) - Method in class org.apache.ignite.ml.tree.leaf.MostCommonDecisionTreeLeafBuilder

Creates new leaf node for given dataset and node predicate.

createLeafStatisticsAggregator() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Creates an instance of Leaf Statistics Aggregator corresponding to RF implementation.

createLeafStatisticsAggregator() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer

Creates an instance of Leaf Statistics Aggregator corresponding to RF implementation.

createLeafStatisticsAggregator() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Creates an instance of Leaf Statistics Aggregator corresponding to RF implementation.

createLeafStatsAggregator(int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.ClassifierLeafValuesComputer

Creates an instance of leaf statistics aggregator in according to concrete algorithm based on RandomForest.

createLeafStatsAggregator(int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

Creates an instance of leaf statistics aggregator in according to concrete algorithm based on RandomForest.

createLeafStatsAggregator(int) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.RegressionLeafValuesComputer

Creates an instance of leaf statistics aggregator in according to concrete algorithm based on RandomForest.

createSimpleDataset(DatasetBuilder<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.

createSimpleDataset(Ignite, IgniteCache<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleDataset using the specified partCtxBuilder and featureExtractor.

createSimpleDataset(DatasetBuilder<K, V>, LearningEnvironmentBuilder, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleDataset using the specified featureExtractor.

createSimpleDataset(Ignite, IgniteCache<K, V>, LearningEnvironmentBuilder, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleDataset using the specified featureExtractor.

createSimpleDataset(Ignite, IgniteCache<K, V>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleDataset using the specified featureExtractor.

createSimpleDataset(Map<K, V>, int, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of local SimpleDataset using the specified partCtxBuilder and featureExtractor.

createSimpleDataset(Map<K, V>, int, LearningEnvironmentBuilder, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of local SimpleDataset using the specified featureExtractor.

createSimpleLabeledDataset(DatasetBuilder<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

createSimpleLabeledDataset(Ignite, IgniteCache<K, V>, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

createSimpleLabeledDataset(DatasetBuilder<K, V>, LearningEnvironmentBuilder, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.

createSimpleLabeledDataset(Ignite, LearningEnvironmentBuilder, IgniteCache<K, V>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of distributed SimpleLabeledDataset using the specified featureExtractor and lbExtractor.

createSimpleLabeledDataset(Map<K, V>, int, LearningEnvironmentBuilder, PartitionContextBuilder<K, V, C>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of local SimpleLabeledDataset using the specified partCtxBuilder, featureExtractor and lbExtractor.

createSimpleLabeledDataset(Map<K, V>, LearningEnvironmentBuilder, int, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.dataset.DatasetFactory

Creates a new instance of local SimpleLabeledDataset using the specified featureExtractor and lbExtractor.

createVector(int) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Create an instance of vector.

createVector(int) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Create an instance of vector.

cross(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the cross product of this vector and the other vector.

cross(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the cross product of this vector and the other vector.

cross(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the cross product of this vector and the other vector.

CrossOverJob - Class in org.apache.ignite.ml.genetic

Responsible for performing 'crossover' genetic operation for 2 X 'parent' chromosomes.

CrossOverJob(Long, Long, double) - Constructor for class org.apache.ignite.ml.genetic.CrossOverJob

CrossoverStrategy - Enum in org.apache.ignite.ml.util.genetic

Represents the crossover strategy depending of locus point amount.

CrossOverTask - Class in org.apache.ignite.ml.genetic

Responsible for assigning 2 X 'parent' chromosomes to produce 2 X 'child' chromosomes.

CrossOverTask(GAConfiguration) - Constructor for class org.apache.ignite.ml.genetic.CrossOverTask

CrossValidation<M extends IgniteModel<Vector,L>,L,K,V> - Class in org.apache.ignite.ml.selection.cv

Cross validation score calculator.

CrossValidation() - Constructor for class org.apache.ignite.ml.selection.cv.CrossValidation

CrossValidationResult - Class in org.apache.ignite.ml.selection.cv

Represents the cross validation procedure result, wraps score and values of hyper parameters associated with these values.

CrossValidationResult() - Constructor for class org.apache.ignite.ml.selection.cv.CrossValidationResult

curry(BiFunction<A, B, C>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Curry bi-function.

curry(IgniteBiFunction<A, B, C>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Transform bi-function of the form (a, b) -> c into a function of form a -> (b -> c).

curry(IgniteTriFunction<A, B, C, D>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Transform tri-function of the form (a, b, c) -> d into a function of form a -> (b -> (c -> d)).

CustomMLLogger - Class in org.apache.ignite.ml.environment.logging

MLLogger implementation based on IgniteLogger.

D

data() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

Gets underlying data, if StorageOpsMetrics.isArrayBased() returns false this method return copy of data.

data() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

Gets underlying data, if StorageOpsMetrics.isArrayBased() returns false this method return copy of data.

data() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

Gets underlying data, if StorageOpsMetrics.isArrayBased() returns false this method return copy of data.

data() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

Gets underlying data, if StorageOpsMetrics.isArrayBased() returns false this method return copy of data.

data() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

Gets underlying array if StorageOpsMetrics.isArrayBased() returns true and all values in vector are Numbers.

data() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

Gets underlying array if StorageOpsMetrics.isArrayBased() returns true and all values in vector are Numbers.

data() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Gets underlying array if StorageOpsMetrics.isArrayBased() returns true and all values in vector are Numbers.

data() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

Gets underlying array if StorageOpsMetrics.isArrayBased() returns true and all values in vector are Numbers.

data() - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

Gets underlying array if StorageOpsMetrics.isArrayBased() returns true and all values in vector are Numbers.

data - Variable in class org.apache.ignite.ml.structures.Dataset

Data to keep.

data() - Method in class org.apache.ignite.ml.structures.Dataset

Dataset<C extends Serializable,D extends AutoCloseable> - Interface in org.apache.ignite.ml.dataset

A dataset providing an API that allows to perform generic computations on a distributed data represented as a set of partitions distributed across a cluster or placed locally.

Dataset<Row extends DatasetRow> - Class in org.apache.ignite.ml.structures

Class for set of vectors.

Dataset() - Constructor for class org.apache.ignite.ml.structures.Dataset

Default constructor (required by Externalizable).

Dataset(Row[], FeatureMetadata[]) - Constructor for class org.apache.ignite.ml.structures.Dataset

Creates new Dataset by given data.

Dataset(Row[], String[], int) - Constructor for class org.apache.ignite.ml.structures.Dataset

Creates new Dataset by given data.

Dataset(Row[], int) - Constructor for class org.apache.ignite.ml.structures.Dataset

Creates new Dataset by given data.

Dataset(Row[]) - Constructor for class org.apache.ignite.ml.structures.Dataset

Creates new Dataset by given data.

Dataset(int, int, String[], boolean) - Constructor for class org.apache.ignite.ml.structures.Dataset

Creates new Dataset and initialized with empty data structure.

DatasetAffinityFunctionWrapper - Class in org.apache.ignite.ml.dataset.impl.cache.util

Affinity function wrapper that uses key as a partition index and delegates all other functions to specified delegate.

DatasetAffinityFunctionWrapper(AffinityFunction) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

Constructs a new instance of affinity function wrapper.

DatasetBuilder<K,V> - Interface in org.apache.ignite.ml.dataset

A builder constructing instances of a Dataset.

DatasetFactory - Class in org.apache.ignite.ml.dataset

Factory providing a client facing API that allows to construct basic and the most frequently used types of dataset.

DatasetFactory() - Constructor for class org.apache.ignite.ml.dataset.DatasetFactory

datasetMapping - Variable in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Dataset mapping.

DatasetMapping<L1,L2> - Interface in org.apache.ignite.ml.composition

This class represents dataset mapping.

DatasetRow<V extends Vector> - Class in org.apache.ignite.ml.structures

Class to keep one observation in dataset.

DatasetRow() - Constructor for class org.apache.ignite.ml.structures.DatasetRow

Default constructor (required by Externalizable).

DatasetRow(V) - Constructor for class org.apache.ignite.ml.structures.DatasetRow

DatasetTrainer<M extends IgniteModel,L> - Class in org.apache.ignite.ml.trainers

Interface for trainers.

DatasetTrainer() - Constructor for class org.apache.ignite.ml.trainers.DatasetTrainer

DatasetTrainer.EmptyDatasetException - Exception in org.apache.ignite.ml.trainers

EmptyDataset exception.

DatasetWrapper<C extends Serializable,D extends AutoCloseable> - Class in org.apache.ignite.ml.dataset.primitive

A dataset wrapper that allows to introduce new functionality based on common compute methods.

DatasetWrapper(Dataset<C, D>) - Constructor for class org.apache.ignite.ml.dataset.primitive.DatasetWrapper

Constructs a new instance of dataset wrapper that delegates compute actions to the actual delegate.

DataStreamGenerator - Interface in org.apache.ignite.ml.util.generators

Provides general interface for generation of pseudorandom vectors according to shape defined by logic of specific data stream generator.

dataTtl() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Returns partition data time-to-live in seconds (-1 for an infinite lifetime).

DebugCrossValidation<M extends IgniteModel<Vector,L>,L,K,V> - Class in org.apache.ignite.ml.selection.cv

Cross validation score calculator.

DebugCrossValidation() - Constructor for class org.apache.ignite.ml.selection.cv.DebugCrossValidation

DecisionTree<T extends ImpurityMeasure<T>> - Class in org.apache.ignite.ml.tree

Distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

DecisionTreeClassificationTrainer - Class in org.apache.ignite.ml.tree

Decision tree classifier based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

DecisionTreeClassificationTrainer(int, double) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Constructs a new decision tree classifier with default impurity function compressor.

DecisionTreeClassificationTrainer() - Constructor for class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Constructs a new decision tree classifier with default impurity function compressor and default maxDeep = 5 and minImpurityDecrease = 0.

DecisionTreeClassificationTrainer(int, double, StepFunctionCompressor<GiniImpurityMeasure>) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Constructs a new instance of decision tree classifier.

DecisionTreeConditionalNode - Class in org.apache.ignite.ml.tree

Decision tree conditional (non-leaf) node.

DecisionTreeConditionalNode(int, double, DecisionTreeNode, DecisionTreeNode, DecisionTreeNode) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

Constructs a new instance of decision tree conditional node.

DecisionTreeData - Class in org.apache.ignite.ml.tree.data

A partition data of the containing matrix of features and vector of labels stored in heap with index on features.

DecisionTreeData(double[][], double[], boolean) - Constructor for class org.apache.ignite.ml.tree.data.DecisionTreeData

Constructs a new instance of decision tree data.

DecisionTreeDataBuilder<K,V,C extends Serializable> - Class in org.apache.ignite.ml.tree.data

A partition data builder that makes DecisionTreeData.

DecisionTreeDataBuilder(Preprocessor<K, V>, boolean) - Constructor for class org.apache.ignite.ml.tree.data.DecisionTreeDataBuilder

Constructs a new instance of decision tree data builder.

DecisionTreeLeafBuilder - Interface in org.apache.ignite.ml.tree.leaf

Base interface for decision tree leaf builders.

DecisionTreeLeafNode - Class in org.apache.ignite.ml.tree

Decision tree leaf node which contains value.

DecisionTreeLeafNode(double) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeLeafNode

Constructs a new decision tree leaf node.

DecisionTreeNode - Interface in org.apache.ignite.ml.tree

Base interface for decision tree nodes.

DecisionTreeRegressionTrainer - Class in org.apache.ignite.ml.tree

Decision tree regressor based on distributed decision tree trainer that allows to fit trees using row-partitioned dataset.

DecisionTreeRegressionTrainer(int, double) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer

Constructs a new decision tree regressor with default impurity function compressor.

DecisionTreeRegressionTrainer(int, double, StepFunctionCompressor<MSEImpurityMeasure>) - Constructor for class org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer

Constructs a new decision tree regressor.

DEFAULT_NUMBER_OF_RETRIES - Static variable in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Default number of retries for the case when one of partitions not found on the node where loading is performed.

DEFAULT_TRAINER_ENV - Static variable in interface org.apache.ignite.ml.environment.LearningEnvironment

Default environment

DEFAULT_VALUE - Static variable in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Object for denoting default value of feature mapping.

defaultBuilder() - Static method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Get default LearningEnvironmentBuilder.

DefaultLabelVectorizer(C...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.DefaultLabelVectorizer

Creates an instance of Vectorizer.

DefaultLearningEnvironmentBuilder - Class in org.apache.ignite.ml.environment

Builder for LearningEnvironment.

DefaultModelStorage - Class in org.apache.ignite.ml.inference.storage.model

Default implementation of ModelStorage that can use any ModelStorageProvider as a backend storage system.

DefaultModelStorage(ModelStorageProvider) - Constructor for class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Constructs a new instance of Ignite model storage.

DefaultParallelismStrategy - Class in org.apache.ignite.ml.environment.parallelism

All task should be processed by default thread pool.

DefaultParallelismStrategy() - Constructor for class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy

DefaultParallelismStrategy.FutureWrapper<T> - Class in org.apache.ignite.ml.environment.parallelism

Wrapper for future class.

defaultValue(Double) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer.Mapping

Default value for new feature values.

delegate - Variable in class org.apache.ignite.ml.dataset.primitive.DatasetWrapper

Delegate that performs compute actions.

delegate() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

delegate() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

DelegatingNamedVector - Class in org.apache.ignite.ml.math.primitives.vector.impl

Delegating named vector that delegates all operations to underlying vector and adds implementation of NamedVector functionality using embedded map that maps string index on real integer index.

DelegatingNamedVector() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

Constructs a new instance of delegating named vector.

DelegatingNamedVector(Vector, Map<String, Integer>) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

Constructs a new instance of delegating named vector.

DelegatingVector - Class in org.apache.ignite.ml.math.primitives.vector.impl

Convenient class that can be used to add decorations to an existing vector.

DelegatingVector() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

DelegatingVector(Vector) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

deleteDirectory(Path) - Static method in class org.apache.ignite.ml.inference.util.DirectorySerializer

Removes the specified directory.

deltas - Variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Previous iteration parameters deltas.

DenseMatrix - Class in org.apache.ignite.ml.math.primitives.matrix.impl

Basic implementation for matrix.

DenseMatrix() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(double[][], int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(double[][]) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(double[], int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

DenseMatrix(double[], int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

Build new matrix from flat raw array.

DenseMatrixStorage - Class in org.apache.ignite.ml.math.primitives.matrix.storage

Array based MatrixStorage implementation.

DenseMatrixStorage() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(double[][], int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(double[][]) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(double[], int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseMatrixStorage(double[], int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

DenseVector - Class in org.apache.ignite.ml.math.primitives.vector.impl

Basic implementation for vector.

DenseVector(Serializable[]) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVector(Map<String, Object>) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVector() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVector(int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVector(double[], boolean) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVector(double[]) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

DenseVectorStorage - Class in org.apache.ignite.ml.math.primitives.vector.storage

Array based VectorStorage implementation.

DenseVectorStorage() - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

IMPL NOTE required by Externalizable.

DenseVectorStorage(int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

DenseVectorStorage(double[]) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

DenseVectorStorage(Serializable[]) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

density(double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Calculates the density of the matrix based on supplied criteria.

density(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Calculates the density of the matrix based on supplied criteria.

DeployableObject - Interface in org.apache.ignite.ml.environment.deploy

Represents an final objects from Ignite ML library like models or preprocessors having dependencies that can be custom objects from client side.

DeployingContext - Interface in org.apache.ignite.ml.environment.deploy

Class represents user's class loading environment for specific remote job.

deployingContext() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Returns deploy context instance.

DeployingContextImpl - Class in org.apache.ignite.ml.environment.deploy

Default implementation of DeployingContext class.

DeployingContextImpl() - Constructor for class org.apache.ignite.ml.environment.deploy.DeployingContextImpl

deserialize(Path, byte[]) - Static method in class org.apache.ignite.ml.inference.util.DirectorySerializer

Deserializes directory content.

deserialize(byte[]) - Static method in class org.apache.ignite.ml.util.Utils

Deserialized object represented as a byte array.

destroy() - Method in interface org.apache.ignite.ml.math.Destroyable

Destroys object if managed outside of JVM.

destroy() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Destroys object if managed outside of JVM.

destroy() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Destroys object if managed outside of JVM.

destroy() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Destroys object if managed outside of JVM.

destroy() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Destroys object if managed outside of JVM.

Destroyable - Interface in org.apache.ignite.ml.math

Support for destroying objects that are managed outside of JVM.

determinant() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Returns matrix determinant using Laplace theorem.

determinant() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Return the determinant of the matrix.

determinant() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Returns matrix determinant using Laplace theorem.

differential(double) - Method in interface org.apache.ignite.ml.math.functions.IgniteDifferentiableDoubleToDoubleFunction

Get function differential at a given point.

differential(Vector) - Method in interface org.apache.ignite.ml.math.functions.IgniteDifferentiableVectorToDoubleFunction

Get function differential at a given point.

differentiateByParameters(IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>, Matrix, Matrix) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Compose function in the following way: feed output of this model as input to second argument to loss function.

differentiateByParameters(IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>, Matrix, Matrix) - Method in interface org.apache.ignite.ml.optimization.SmoothParametrized

Compose function in the following way: feed output of this model as input to second argument to loss function.

dimension() - Method in interface org.apache.ignite.ml.math.stat.Distribution

dimension() - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

dimension() - Method in class org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution

DirectorySerializer - Class in org.apache.ignite.ml.inference.util

Utils class that helps to serialize directory content as a has map and then deserialize it.

DirectorySerializer() - Constructor for class org.apache.ignite.ml.inference.util.DirectorySerializer

DiscreteNaiveBayesModel - Class in org.apache.ignite.ml.naivebayes.discrete

Discrete naive Bayes model which predicts result value y belongs to a class C_k, k in [0..K] as {@code p(C_k,y) =x_1*p_k1^x *...

DiscreteNaiveBayesModel(double[][][], double[], double[], double[][], DiscreteNaiveBayesSumsHolder) - Constructor for class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

DiscreteNaiveBayesSumsHolder - Class in org.apache.ignite.ml.naivebayes.discrete

Service class is used to calculate amount of values which are below the threshold.

DiscreteNaiveBayesSumsHolder() - Constructor for class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesSumsHolder

DiscreteNaiveBayesTrainer - Class in org.apache.ignite.ml.naivebayes.discrete

Trainer for the Discrete naive Bayes classification model.

DiscreteNaiveBayesTrainer() - Constructor for class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

DiscreteRandomProducer - Class in org.apache.ignite.ml.util.generators.primitives.scalar

Pseudorandom producer generating values from user provided discrete distribution.

DiscreteRandomProducer(double...) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Creates an instance of DiscreteRandomProducer.

DiscreteRandomProducer(long, double...) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Creates an instance of DiscreteRandomProducer.

distanceMeasure() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Distance measure.

distanceMeasure - Variable in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Distance measure.

distanceMeasure - Variable in class org.apache.ignite.ml.knn.KNNModel

Distance measure.

distanceMeasure - Variable in class org.apache.ignite.ml.knn.KNNTrainer

Distance measure.

distanceMeasure - Variable in class org.apache.ignite.ml.knn.NNClassificationModel

Distance measure.

DistanceMeasure - Interface in org.apache.ignite.ml.math.distances

This class is based on the corresponding class from Apache Common Math lib.

Distribution - Interface in org.apache.ignite.ml.math.stat

Interface for distributions.

DistributionComputer - Interface in org.apache.ignite.ml.dataset.feature

Interface specifies an object that can compute some discrete distribution.

distributionId() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.VectorWithDistributionId

DistributionMixture<C extends Distribution> - Class in org.apache.ignite.ml.math.stat

Mixture of distributions class where each component has own probability and probability of input vector can be computed as a sum of likelihoods of each component.

DistributionMixture(Vector, List<C>) - Constructor for class org.apache.ignite.ml.math.stat.DistributionMixture

Creates an instance of DistributionMixture.

distributions() - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

div(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns a / b.

divide(double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Divides each value in this matrix by the argument.

divide(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Divides each value in this matrix by the argument.

divide(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing values from this vector divided by the argument.

divide(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing values from this vector divided by the argument.

divide(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing values from this vector divided by the argument.

dot(Vector, Vector) - Static method in class org.apache.ignite.ml.math.Blas

Returns dot product of vectors x and y.

dot(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets dot product of two vectors.

dot(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets dot product of two vectors.

dot(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets dot product of two vectors.

dotSelf() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

DoubleArrayVectorizer<K> - Class in org.apache.ignite.ml.dataset.feature.extractor.impl

Vectorizer on arrays of doubles.

DoubleArrayVectorizer(Integer...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer

Creates an instance of Vectorizer.

DummyVectorizer<K> - Class in org.apache.ignite.ml.dataset.feature.extractor.impl

Vectorizer on Vector.

DummyVectorizer(Integer...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer

Creates an instance of Vectorizer.

duplicateRandomFeatures(int) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Increase vectors of generator by increaseSize and sets to new values random selected feature values from already set components.

duplicateRandomFeatures(int, Long) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Increase vectors of generator by increaseSize and sets to new values random selected feature values from already set components.

E

elementWiseMinus(Vector, Vector) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Performs in-place vector subtraction.

elementWiseMinus(Matrix, Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Performs in-place matrix subtraction.

elementWiseTimes(Vector, Vector) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Performs in-place vector multiplication.

elementWiseTimes(Matrix, Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Performs in-place matrix multiplication.

EmptyContext - Class in org.apache.ignite.ml.dataset.primitive.context

An empty partition context.

EmptyContext() - Constructor for class org.apache.ignite.ml.dataset.primitive.context.EmptyContext

EmptyContextBuilder<K,V> - Class in org.apache.ignite.ml.dataset.primitive.builder.context

A partition context builder that makes EmptyContext.

EmptyContextBuilder() - Constructor for class org.apache.ignite.ml.dataset.primitive.builder.context.EmptyContextBuilder

EmptyDatasetException() - Constructor for exception org.apache.ignite.ml.trainers.DatasetTrainer.EmptyDatasetException

Constructs an instance of EmptyDatasetException.

EmptyFileException - Exception in org.apache.ignite.ml.math.exceptions.knn

Shows empty filename.

EmptyFileException(String) - Constructor for exception org.apache.ignite.ml.math.exceptions.knn.EmptyFileException

Creates new exception.

emptyVector(int, boolean) - Static method in class org.apache.ignite.ml.structures.LabeledVectorSet

encode(ClientConnectionContext, BinaryRawWriterEx) - Method in class org.apache.ignite.ml.inference.storage.model.thinclient.FileRespose

encode(ClientConnectionContext, BinaryRawWriterEx) - Method in class org.apache.ignite.ml.inference.storage.model.thinclient.FilesListResponse

encode(ClientConnectionContext, BinaryRawWriterEx) - Method in class org.apache.ignite.ml.inference.storage.model.thinclient.FileStatResponse

EncoderPartitionData - Class in org.apache.ignite.ml.preprocessing.encoding

Partition data used in Encoder preprocessor.

EncoderPartitionData() - Constructor for class org.apache.ignite.ml.preprocessing.encoding.EncoderPartitionData

Constructs a new instance of String Encoder partition data.

EncoderPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.encoding

Preprocessing function that makes encoding.

EncoderPreprocessor(Map<String, Integer>[], Preprocessor<K, V>, Set<Integer>) - Constructor for class org.apache.ignite.ml.preprocessing.encoding.EncoderPreprocessor

Constructs a new instance of String Encoder preprocessor.

EncoderSortingStrategy - Enum in org.apache.ignite.ml.preprocessing.encoding

Describes Encoder sorting strategy to define mapping of integer values to values of categorical feature .

EncoderTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.encoding

Trainer of the String Encoder and One-Hot Encoder preprocessors.

EncoderTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

EncoderType - Enum in org.apache.ignite.ml.preprocessing.encoding

Describes Encoder preprocessor types to define resulting model in EncoderTrainer.

encodingValues - Variable in class org.apache.ignite.ml.preprocessing.encoding.EncoderPreprocessor

Filling values.

entryExpired(V) - Method in interface org.apache.ignite.ml.util.LRUCacheExpirationListener

Handles entry expiration, is called before value is moved from cache.

envBuilder - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Learning environment builder.

envBuilder - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Learning environment builder.

envBuilder - Variable in class org.apache.ignite.ml.trainers.DatasetTrainer

Learning environment builder.

environment - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Learning Environment.

environment - Variable in class org.apache.ignite.ml.trainers.DatasetTrainer

Learning Environment.

equals(Object) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

equals(Object) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModelFormat

equals(Object) - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

equals(Object) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

equals(Object) - Method in class org.apache.ignite.ml.knn.ann.ANNModelFormat

equals(Object) - Method in class org.apache.ignite.ml.knn.ann.KNNModelFormat

equals(Object) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

equals(Object) - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

equals(Object) - Method in class org.apache.ignite.ml.math.distances.HammingDistance

equals(Object) - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

We ignore guid's for comparisons.

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

equals(Object) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

equals(Object) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

equals(Object) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

equals(Object) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

equals(Object) - Method in class org.apache.ignite.ml.structures.Dataset

equals(Object) - Method in class org.apache.ignite.ml.structures.DatasetRow

equals(Object) - Method in class org.apache.ignite.ml.structures.FeatureMetadata

equals(Object) - Method in class org.apache.ignite.ml.structures.LabeledVector

equals(Object) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

equals(Object) - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeId

error(long, double, double) - Method in class org.apache.ignite.ml.composition.boosting.loss.LogLoss

Error value for model answer.

error(long, double, double) - Method in interface org.apache.ignite.ml.composition.boosting.loss.Loss

Error value for model answer.

error(long, double, double) - Method in class org.apache.ignite.ml.composition.boosting.loss.SquaredError

Error value for model answer.

EuclideanDistance - Class in org.apache.ignite.ml.math.distances

Calculates the L₂ (Euclidean) distance between two points.

EuclideanDistance() - Constructor for class org.apache.ignite.ml.math.distances.EuclideanDistance

evaluate(List<Gene>) - Method in interface org.apache.ignite.ml.genetic.IFitnessFunction

evaluate(IgniteCache<K, V>, IgniteModel<Vector, L>, Preprocessor<K, V>, Metric<L>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metric on the given cache.

evaluate(Map<K, V>, IgniteModel<Vector, L>, Preprocessor<K, V>, Metric<L>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metric on the given cache.

evaluate(IgniteCache<K, V>, IgniteBiPredicate<K, V>, IgniteModel<Vector, L>, Preprocessor<K, V>, Metric<L>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metric on the given cache.

evaluate(Map<K, V>, IgniteBiPredicate<K, V>, IgniteModel<Vector, L>, Preprocessor<K, V>, Metric<L>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metric on the given cache.

evaluate(IgniteCache<K, V>, IgniteModel<Vector, Double>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metrics on the given cache.

evaluate(Map<K, V>, IgniteModel<Vector, Double>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metrics on the given cache.

evaluate(IgniteCache<K, V>, IgniteBiPredicate<K, V>, IgniteModel<Vector, Double>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metrics on the given cache.

evaluate(Map<K, V>, IgniteBiPredicate<K, V>, IgniteModel<Vector, Double>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the given metrics on the given cache.

evaluateRegression(IgniteCache<K, V>, IgniteBiPredicate<K, V>, IgniteModel<Vector, Double>, Preprocessor<K, V>) - Static method in class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

Computes the regression metrics on the given cache.

Evaluator - Class in org.apache.ignite.ml.selection.scoring.evaluator

Evaluator that computes metrics from predictions and ground truth values.

Evaluator() - Constructor for class org.apache.ignite.ml.selection.scoring.evaluator.Evaluator

EvolutionOptimizationStrategy - Class in org.apache.ignite.ml.selection.paramgrid

Represents the Genetic algorithms usage for finding the best set of hyper-parameters.

EvolutionOptimizationStrategy() - Constructor for class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

evolve() - Method in class org.apache.ignite.ml.genetic.GAGrid

Evolve the population

exclude(C...) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Exclude these coordinates from result vector.

execute() - Method in class org.apache.ignite.ml.genetic.CrossOverJob

execute() - Method in class org.apache.ignite.ml.genetic.FitnessJob

execute() - Method in class org.apache.ignite.ml.genetic.MutateJob

execute() - Method in class org.apache.ignite.ml.genetic.RouletteWheelSelectionJob

Perform Roulette Wheel selection

execute() - Method in class org.apache.ignite.ml.genetic.TruncateSelectionJob

exists(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns true if a regular file or directory exist, otherwise false.

exists(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns true if a regular file or directory exist, otherwise false.

Exportable<D> - Interface in org.apache.ignite.ml

Interface for exportable models(IgniteModel).

Exporter<D,P> - Interface in org.apache.ignite.ml

Exporter for models, D - model representation object, P - path to resources.

externalLabelToInternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Maps external representation of label to internal.

externalLabelToInternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBRegressionTrainer

Maps external representation of label to internal.

externalLabelToInternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Maps external representation of label to internal.

externalLbToInternalMapping - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

External label to internal mapping.

extract(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Extract LabeledVector from key and value.

extract(K, V) - Method in interface org.apache.ignite.ml.trainers.FeatureLabelExtractor

Extract LabeledVector from key and value.

extractFeatures(K, V) - Method in interface org.apache.ignite.ml.trainers.FeatureLabelExtractor

Extract features from key and value.

ExtractionUtils - Class in org.apache.ignite.ml.dataset.feature.extractor

Class aggregates helper classes and shortcut-classes with default types and behaviour for Vectorizers.

ExtractionUtils() - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils

ExtractionUtils.ArrayLikeVectorizer<K,V> - Class in org.apache.ignite.ml.dataset.feature.extractor

Vectorizer extracting vectors from array-like structure with finite size and integer coordinates.

ExtractionUtils.DefaultLabelVectorizer<K,V,C extends Serializable> - Class in org.apache.ignite.ml.dataset.feature.extractor

Vectorizer with double-label containing on same level as feature values.

ExtractionUtils.IntCoordVectorizer<K,V> - Class in org.apache.ignite.ml.dataset.feature.extractor

Vectorizer with integer coordinates.

ExtractionUtils.StringCoordVectorizer<K,V> - Class in org.apache.ignite.ml.dataset.feature.extractor

Vectorizer with String-coordinates.

extractLabel(K, V) - Method in interface org.apache.ignite.ml.trainers.FeatureLabelExtractor

Extract label from key and value.

F

f1Score() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns F1-Score is the harmonic mean of Precision and Sensitivity.

factory(MLLogger.VerboseLevel) - Static method in class org.apache.ignite.ml.environment.logging.ConsoleLogger

Returns an instance of ConsoleLogger factory.

factory(IgniteLogger) - Static method in class org.apache.ignite.ml.environment.logging.CustomMLLogger

Returns factory for OnIgniteLogger instantiating.

factory() - Static method in class org.apache.ignite.ml.environment.logging.NoOpLogger

Returns NoOpLogger factory.

fallOut() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Fall-out or False Positive Rate (FPR).

fdr() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns False Discovery Rate (FDR).

feature(String, K, BinaryObject) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Extracts feature value by given coordinate.

feature(Integer, K, double[]) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer

Extracts feature value by given coordinate.

feature(Integer, K, Vector) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer

Extracts feature value by given coordinate.

feature(Integer, K, LabeledVector<L>) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Extracts feature value by given coordinate.

feature(C, K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Extracts feature value by given coordinate.

feature(C, K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.VectorizerAdapter

Extracts feature value by given coordinate.

featureId - Variable in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.BootstrappedVectorsHistogram

Feature id.

featureId - Variable in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogram

Feature id.

featureInSortedOrder(int, int) - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

Returns feature value for kth order statistic for target feature.

FeatureLabelExtractor<K,V,L> - Interface in org.apache.ignite.ml.trainers

Class fro extracting features and vectors from upstream.

FeatureMatrixWithLabelsOnHeapData - Class in org.apache.ignite.ml.dataset.primitive

A partition data of the containing matrix of features and vector of labels stored in heap.

FeatureMatrixWithLabelsOnHeapData(double[][], double[]) - Constructor for class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData

Constructs an instance of FeatureMatrixWithLabelsOnHeapData.

FeatureMatrixWithLabelsOnHeapDataBuilder<K,V,C extends Serializable,CO extends Serializable> - Class in org.apache.ignite.ml.dataset.primitive

A partition data builder that makes DecisionTreeData.

FeatureMatrixWithLabelsOnHeapDataBuilder(Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapDataBuilder

Constructs a new instance of decision tree data builder.

FeatureMeta - Class in org.apache.ignite.ml.dataset.feature

Feature meta class.

FeatureMeta(String, int, boolean) - Constructor for class org.apache.ignite.ml.dataset.feature.FeatureMeta

Create an instance of Feature meta.

FeatureMetadata - Class in org.apache.ignite.ml.structures

Class for feature metadata.

FeatureMetadata() - Constructor for class org.apache.ignite.ml.structures.FeatureMetadata

Default constructor (required by Externalizable).

FeatureMetadata(String) - Constructor for class org.apache.ignite.ml.structures.FeatureMetadata

Creates an instance of Feature Metadata class.

features(int) - Method in class org.apache.ignite.ml.structures.Dataset

Get the features.

features() - Method in class org.apache.ignite.ml.structures.DatasetRow

Get the vector.

FeaturesCountSelectionStrategies - Class in org.apache.ignite.ml.tree.randomforest.data

Class contains a default implementations of some features count selection strategies for random forest.

FeaturesCountSelectionStrategies() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies

featuresInSortedOrder(int, int) - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

Returns vector of original features for kth order statistic for target feature.

FileExporter<D> - Class in org.apache.ignite.ml

Implementation of exporter to/from file.

FileExporter() - Constructor for class org.apache.ignite.ml.FileExporter

FileOrDirectory - Interface in org.apache.ignite.ml.inference.storage.model

Base interface for file or directory ModelStorageProvider works with.

FileParsingException - Exception in org.apache.ignite.ml.math.exceptions.knn

Shows non-parsed data in specific row by given file path.

FileParsingException(String, int, Path) - Constructor for exception org.apache.ignite.ml.math.exceptions.knn.FileParsingException

Creates new exception.

FileRespose - Class in org.apache.ignite.ml.inference.storage.model.thinclient

Response with file's data.

FileRespose(long, byte[]) - Constructor for class org.apache.ignite.ml.inference.storage.model.thinclient.FileRespose

Creates an instance of file response.

FilesListResponse - Class in org.apache.ignite.ml.inference.storage.model.thinclient

Response with list of files in directory.

FilesListResponse(long, Set<String>) - Constructor for class org.apache.ignite.ml.inference.storage.model.thinclient.FilesListResponse

Creates an instance of files list response.

FileStat - Class in org.apache.ignite.ml.inference.storage.model

File statistics aggregator.

FileStat(boolean, long, int) - Constructor for class org.apache.ignite.ml.inference.storage.model.FileStat

Creates an instance of stat file.

FileStatResponse - Class in org.apache.ignite.ml.inference.storage.model.thinclient

File statistics repsponse.

FileStatResponse(long, FileStat) - Constructor for class org.apache.ignite.ml.inference.storage.model.thinclient.FileStatResponse

Create an instance of file statistics.

FileSystemModelReader - Class in org.apache.ignite.ml.inference.reader

Model reader that reads directory or file and serializes it using DirectorySerializer.

FileSystemModelReader(String) - Constructor for class org.apache.ignite.ml.inference.reader.FileSystemModelReader

Constructs a new instance of directory model reader.

fill(double, int) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Create new vector of specified size n with specified value.

FILL_CACHE_BATCH_SIZE - Static variable in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Size of batch for IgniteCache.putAll(Map).

fillCacheWith(double[][]) - Method in class org.apache.ignite.ml.util.SandboxMLCache

Fills cache with data and returns it.

fillCacheWith(MLSandboxDatasets) - Method in class org.apache.ignite.ml.util.SandboxMLCache

Fills cache with data and returns it.

fillCacheWithCustomKey(int, IgniteCache<K, LabeledVector<Double>>, Function<LabeledVector<Double>, K>) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Fills given cache with labeled vectors from this generator and user defined mapper from vectors to keys.

fillCacheWithVecHashAsKey(int, IgniteCache<Integer, LabeledVector<Double>>) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Fills given cache with labeled vectors from this generator as values and their hashcodes as keys.

fillCacheWithVecUUIDAsKey(int, IgniteCache<UUID, LabeledVector<Double>>) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Fills given cache with labeled vectors from this generator as values and random UUIDs as keys

filter - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Filter.

filter(TreeFilter) - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

Filters objects and returns only data that passed filter.

filter(TreeFilter) - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

Creates projection of current index in according to TreeFilter.

filter(IgnitePredicate<Vector>) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Filters values of vector generator using predicate.

findBestSplit() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

Returns best split point computed on histogram if it exists.

findBestSplit() - Method in interface org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityComputer

Returns best split point computed on histogram if it exists.

findBestSplit() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer.NodeImpurityHistograms

Find best split point, based on feature statistics.

findBestSplit() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

Returns best split point computed on histogram if it exists.

findKClosest(int, Vector) - Method in class org.apache.ignite.ml.knn.KNNModel

Finds k closest elements to the specified point.

findKClosest(int, Vector) - Method in class org.apache.ignite.ml.knn.utils.indices.ArraySpatialIndex

Finds k closest elements to the specified point.

findKClosest(int, Vector) - Method in class org.apache.ignite.ml.knn.utils.indices.BallTreeSpatialIndex

Finds k closest elements to the specified point.

findKClosest(int, Vector) - Method in class org.apache.ignite.ml.knn.utils.indices.KDTreeSpatialIndex

Finds k closest elements to the specified point.

findKClosest(int, Vector) - Method in interface org.apache.ignite.ml.knn.utils.indices.SpatialIndex

Finds k closest elements to the specified point.

firstModel() - Method in class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Get first model.

fit(Ignite, IgniteCache<K, V>) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Fits the pipeline to the input cache.

fit(Map<K, V>, int) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Fits the pipeline to the input mock data.

fit(DatasetBuilder) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Fits the pipeline to the input dataset builder.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(Ignite, IgniteCache<K, V>, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, Ignite, IgniteCache<K, V>, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, Map<K, V>, int, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(Map<K, V>, int, Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Fits preprocessor.

fit(LearningEnvironmentBuilder, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerTrainer

Fits preprocessor.

fit(DatasetBuilder<Object, BinaryObject>, String, String, String) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Fits prediction model on a data storen in binary format.

fit(DatasetBuilder<K, ? extends ObjectSubjectRatingTriplet<O, S>>) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Fits prediction model.

fit(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(DatasetBuilder<K, V>, Preprocessor<K, V>, LearningEnvironment) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(Ignite, IgniteCache<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(Map<K, V>, int, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(Map<K, V>, IgniteBiPredicate<K, V>, int, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fit(Dataset<EmptyContext, DecisionTreeData>) - Method in class org.apache.ignite.ml.tree.DecisionTree

FitnessJob - Class in org.apache.ignite.ml.genetic

Responsible for performing fitness evaluation on an individual chromosome

FitnessJob(Long, IFitnessFunction) - Constructor for class org.apache.ignite.ml.genetic.FitnessJob

FitnessTask - Class in org.apache.ignite.ml.genetic

Responsible for fitness operation

FitnessTask(GAConfiguration) - Constructor for class org.apache.ignite.ml.genetic.FitnessTask

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.multiclass.OneVsRestTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.tree.DecisionTree

Trains model based on the specified data.

fitWithInitializedDeployingContext(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Trains model based on the specified data.

flatten(double[][], int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Fmeasure<L> - Class in org.apache.ignite.ml.selection.scoring.metric.classification

F-measure calculator.

Fmeasure(L) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.Fmeasure

The class of interest or positive class.

fn() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

foldColumns(IgniteFunction<Vector, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Collects the results of applying a given function to all columns in this matrix.

foldColumns(IgniteFunction<Vector, Double>) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Collects the results of applying a given function to all columns in this matrix.

foldMap(IgniteBiFunction<T, Double, T>, IgniteDoubleFunction<Double>, T) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Folds this matrix into a single value.

foldMap(IgniteBiFunction<T, Double, T>, IgniteDoubleFunction<Double>, T) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Folds this matrix into a single value.

foldMap(IgniteBiFunction<T, Double, T>, IgniteDoubleFunction<Double>, T) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Folds this vector into a single value.

foldMap(Vector, IgniteBiFunction<T, Double, T>, IgniteBiFunction<Double, Double, Double>, T) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Combines & maps two vector and folds them into a single value.

foldMap(IgniteBiFunction<T, Double, T>, IgniteDoubleFunction<Double>, T) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Folds this vector into a single value.

foldMap(Vector, IgniteBiFunction<T, Double, T>, IgniteBiFunction<Double, Double, Double>, T) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Combines & maps two vector and folds them into a single value.

foldMap(IgniteBiFunction<T, Double, T>, IgniteDoubleFunction<Double>, T) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Folds this vector into a single value.

foldMap(Vector, IgniteBiFunction<T, Double, T>, IgniteBiFunction<Double, Double, Double>, T) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Combines & maps two vector and folds them into a single value.

foldRows(IgniteFunction<Vector, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Collects the results of applying a given function to all rows in this matrix.

foldRows(IgniteFunction<Vector, Double>) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Collects the results of applying a given function to all rows in this matrix.

forwardPass(Matrix, MLPState, boolean) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Perform forward pass and if needed write state of outputs of each layer.

fp() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

fromList(List<Vector>, boolean) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Functions - Class in org.apache.ignite.ml.math.functions

Compatibility with Apache Mahout.

Functions() - Constructor for class org.apache.ignite.ml.math.functions.Functions

FutureWrapper(Future<T>) - Constructor for class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

Create an instance of FutureWrapper.

G

GAConfiguration - Class in org.apache.ignite.ml.genetic.parameter

Maintains configuration parameters to be used in genetic algorithm
NOTE: Default selectionMethod is SELECTION_METHOD_TRUNCATION Default truncateRate is .10 More selectionMethods will be introduced in future releases.

GAConfiguration() - Constructor for class org.apache.ignite.ml.genetic.parameter.GAConfiguration

GAGrid - Class in org.apache.ignite.ml.genetic

Central class responsible for orchestrating distributive Genetic Algorithm.

GAGrid(GAConfiguration, Ignite) - Constructor for class org.apache.ignite.ml.genetic.GAGrid

GAGridConstants - Interface in org.apache.ignite.ml.genetic.parameter

GAGridConstants

GAGridConstants.SELECTION_METHOD - Enum in org.apache.ignite.ml.genetic.parameter

Selection Method type

GAGridFunction - Class in org.apache.ignite.ml.genetic.functions

Responsible for providing custom SQL functions to retrieve optimization results.

GAGridFunction() - Constructor for class org.apache.ignite.ml.genetic.functions.GAGridFunction

GAGridUtils - Class in org.apache.ignite.ml.genetic.utils

GA Grid Helper routines

GAGridUtils() - Constructor for class org.apache.ignite.ml.genetic.utils.GAGridUtils

gauss(Vector, Vector, Long) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of vectors from multidimension gauss distribution.

gauss(Vector, Vector) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of vectors from multidimension gauss distribution.

GaussianMixtureDataStream - Class in org.apache.ignite.ml.util.generators.standard

Data stream generator representing gaussian mixture.

GaussianMixtureDataStream.Builder - Class in org.apache.ignite.ml.util.generators.standard

Builder for gaussian mixture.

GaussianNaiveBayesModel - Class in org.apache.ignite.ml.naivebayes.gaussian

Simple naive Bayes model which predicts result value y belongs to a class C_k, k in [0..K] as {@code p(C_k,y) = p(C_k)*p(y_1,C_k) *...

GaussianNaiveBayesModel(double[][], double[][], double[], double[], GaussianNaiveBayesSumsHolder) - Constructor for class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

GaussianNaiveBayesTrainer - Class in org.apache.ignite.ml.naivebayes.gaussian

Trainer for the naive Bayes classification model.

GaussianNaiveBayesTrainer() - Constructor for class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

GaussRandomProducer - Class in org.apache.ignite.ml.util.generators.primitives.scalar

Pseudorandom producer generating values from gauss distribution.

GaussRandomProducer() - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer

Creates an instance of GaussRandomProducer with mean = 0 and variance = 1.0.

GaussRandomProducer(long) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer

Creates an instance of GaussRandomProducer with mean = 0 and variance = 1.0.

GaussRandomProducer(double, double) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer

Creates an instance of GaussRandomProducer.

GaussRandomProducer(double, double, long) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer

Creates an instance of GaussRandomProducer.

GDBBinaryClassifierOnTreesTrainer - Class in org.apache.ignite.ml.tree.boosting

Implementation of Gradient Boosting Classifier Trainer on trees.

GDBBinaryClassifierOnTreesTrainer(double, Integer, int, double) - Constructor for class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Constructs instance of GDBBinaryClassifierOnTreesTrainer.

GDBBinaryClassifierTrainer - Class in org.apache.ignite.ml.composition.boosting

Trainer for binary classifier using Gradient Boosting.

GDBBinaryClassifierTrainer(double, Integer) - Constructor for class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Constructs instance of GDBBinaryClassifierTrainer.

GDBBinaryClassifierTrainer(double, Integer, Loss) - Constructor for class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Constructs instance of GDBBinaryClassifierTrainer.

GDBLearningStrategy - Class in org.apache.ignite.ml.composition.boosting

Learning strategy for gradient boosting.

GDBLearningStrategy() - Constructor for class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

GDBModel(List<? extends IgniteModel<Vector, Double>>, WeightedPredictionsAggregator, IgniteFunction<Double, Double>) - Constructor for class org.apache.ignite.ml.composition.boosting.GDBTrainer.GDBModel

Creates an instance of GDBModel.

GDBOnTreesLearningStrategy - Class in org.apache.ignite.ml.tree.boosting

Gradient boosting on trees specific learning strategy reusing learning dataset with index between several learning iterations.

GDBOnTreesLearningStrategy(boolean) - Constructor for class org.apache.ignite.ml.tree.boosting.GDBOnTreesLearningStrategy

Create an instance of learning strategy.

GDBRegressionOnTreesTrainer - Class in org.apache.ignite.ml.tree.boosting

Implementation of Gradient Boosting Regression Trainer on trees.

GDBRegressionOnTreesTrainer(double, Integer, int, double) - Constructor for class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Constructs instance of GDBRegressionOnTreesTrainer.

GDBRegressionTrainer - Class in org.apache.ignite.ml.composition.boosting

Trainer for regressor using Gradient Boosting.

GDBRegressionTrainer(double, Integer) - Constructor for class org.apache.ignite.ml.composition.boosting.GDBRegressionTrainer

Constructs instance of GDBRegressionTrainer.

GDBTrainer - Class in org.apache.ignite.ml.composition.boosting

Abstract Gradient Boosting trainer.

GDBTrainer(double, Integer, Loss) - Constructor for class org.apache.ignite.ml.composition.boosting.GDBTrainer

Constructs GDBTrainer instance.

GDBTrainer.GDBModel - Class in org.apache.ignite.ml.composition.boosting

GDB model.

gemm(double, Matrix, Matrix, double, Matrix) - Static method in class org.apache.ignite.ml.math.Blas

For the moment we have no flags indicating if matrix is transposed or not.

gemv(double, Matrix, Vector, double, Vector) - Static method in class org.apache.ignite.ml.math.Blas

y := alpha * A * x + beta * y.

Gene - Class in org.apache.ignite.ml.genetic

Represents the discrete parts of a potential solution (ie: Chromosome) Gene is a container for a POJO that developer will implement.

Gene(Object) - Constructor for class org.apache.ignite.ml.genetic.Gene

object Object parameter.

GENE_CACHE - Static variable in interface org.apache.ignite.ml.genetic.parameter.GAGridConstants

populationCache constant

geneCache() - Static method in class org.apache.ignite.ml.genetic.cache.GeneCacheConfig

GeneCacheConfig - Class in org.apache.ignite.ml.genetic.cache

Cache configuration for GAGridConstants.GENE_CACHE cache maintains full population of genes.

GeneCacheConfig() - Constructor for class org.apache.ignite.ml.genetic.cache.GeneCacheConfig

generate() - Method in class org.apache.ignite.ml.selection.paramgrid.ParameterSetGenerator

Returns the list of tuples presented as arrays.

generateFeatureNames() - Method in class org.apache.ignite.ml.structures.Dataset

GeneticAlgorithm - Class in org.apache.ignite.ml.util.genetic

This class is an entry point to use Genetic Algorithm to solve optimization problem.

GeneticAlgorithm(List<Double[]>) - Constructor for class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

get() - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

get(long, TimeUnit) - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

get() - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

get(long, TimeUnit) - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

get(String) - Method in class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Returns model descriptor saved for the specified model identifier.

get(String) - Method in class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

Returns model descriptor saved for the specified model identifier.

get(String) - Method in interface org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorage

Returns model descriptor saved for the specified model identifier.

get(String) - Method in class org.apache.ignite.ml.inference.storage.model.IgniteModelStorageProvider

Returns file or directory associated with the specified path.

get(String) - Method in class org.apache.ignite.ml.inference.storage.model.LocalModelStorageProvider

Returns file or directory associated with the specified path.

get(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorageProvider

Returns file or directory associated with the specified path.

get(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the matrix value at the provided location.

get() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix.Element

Gets element's value.

get(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the matrix value at the provided location.

get(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

get(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

get(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

get(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the value at specified index.

get(String) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

Returns element with specified string index.

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the value at specified index.

get(String) - Method in interface org.apache.ignite.ml.math.primitives.vector.NamedVector

Returns element with specified string index.

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

Gets element from vector by index and cast it to double.

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

Gets element from vector by index and cast it to double.

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Gets element from vector by index and cast it to double.

get(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

Gets element from vector by index and cast it to double.

get() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector.Element

Gets element's value.

get(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the value at specified index.

get(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

Gets element from vector by index and cast it to double.

get(int) - Method in class org.apache.ignite.ml.structures.DatasetRow

Gets the value at specified index.

get() - Method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

get() - Method in class org.apache.ignite.ml.util.generators.primitives.scalar.GaussRandomProducer

get() - Method in class org.apache.ignite.ml.util.generators.primitives.scalar.UniformRandomProducer

get() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.ParametricVectorGenerator

get() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily

getAcond() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getAmountOfClusters() - Method in interface org.apache.ignite.ml.clustering.kmeans.ClusterizationModel

Gets the clusters count.

getAmountOfClusters() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Gets the clusters count.

getAmountOfClusters() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Gets the amount of clusters.

getAmountOfEliteChromosomes() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getAmountOfGenerations() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getAmountOfIterations() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Get the amount of outer iterations of SCDA algorithm.

getAmountOfLocIterations() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Get the amount of local iterations of SCDA algorithm.

getAnorm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getArchSupplier() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the multilayer perceptron architecture supplier that defines layers and activators.

getArnorm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getAsyncModel(Ignite, String, AsyncModelBuilder) - Static method in class org.apache.ignite.ml.inference.IgniteModelStorageUtil

Retrieves Ignite model by name using asynchronous model builder.

getAttribute(String) - Method in interface org.apache.ignite.ml.math.MetaAttributes

Gets meta attribute with given name.

getAverageFitness() - Method in class org.apache.ignite.ml.util.genetic.Population

Returns the average fitness of population or Double.NaN if not all fitnesses are calculated for all chromosomes.

getBatchSize() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the batch size (per every partition).

getBatchSize() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Get the batch size.

getBatchSize() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Get the batch size.

getBest(String) - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Gets the best value for the specific hyper parameter.

getBestAvgScore() - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Gets the the average value of best score array.

getBestHyperParams() - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Gets the best hyper parameters set.

getBestScore() - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Gets the best score for the specific hyper parameter.

getBias() - Method in class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

getBucketId(Double) - Method in class org.apache.ignite.ml.dataset.feature.BucketMeta

Returns bucket id for feature value.

getBucketThresholds() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

getCandidates() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

getCandidates() - Method in class org.apache.ignite.ml.knn.ann.ANNModelFormat

getCenters() - Method in interface org.apache.ignite.ml.clustering.kmeans.ClusterizationModel

Get cluster centers.

getCenters() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Get cluster centers.

getCenters() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModelFormat

getCentroidStat() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer.TotalCostAndCounts

getCentroindsStat() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

getChromosome(int) - Method in class org.apache.ignite.ml.util.genetic.Population

Returns an individual chromosome.

getChromosome(Integer) - Method in class org.apache.ignite.ml.util.genetic.Population

Returns the chromosome by given index.

getChromosomeCriteria() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

retrieve the ChromosomeCriteria

getChromosomeLen() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the chromosome length

getChromosomes(Ignite, String) - Static method in class org.apache.ignite.ml.genetic.utils.GAGridUtils

Retrieve chromosomes

getClassProbabilities() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

getClassVoteForVector(boolean, double) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

getClassWithMaxVotes(Map<Double, Double>) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

getClsProbabilities() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

getCntOfValues() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

getCol(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Get a specific row from matrix.

getCol(int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Get a specific row from matrix.

getCol() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

getColumns() - Method in class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

getColumns() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

Returns number of columns in dataset.

getCompositionWeights() - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

getContext(Ignite, String, int) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Extracts partition context from the Ignite Cache.

getCopiedOriginalLabels() - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

getCriteria() - Method in class org.apache.ignite.ml.genetic.parameter.ChromosomeCriteria

Retrieve criteria

getCrossingoverProbability() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getCrossOverRate() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the cross over rate

getCrossoverStgy() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getCtx() - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDataset

getData(Ignite, String, IgniteBiPredicate<K, V>, UpstreamTransformerBuilder, String, UUID, PartitionDataBuilder<K, V, C, D>, LearningEnvironment, boolean) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Extracts partition data from the local storage, if it's not found in local storage recovers this data from a partition upstream and context.

getData() - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDataset

getDatasetCache() - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

getDependencies() - Method in class org.apache.ignite.ml.clustering.gmm.GmmModel

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.composition.bagging.BaggedModel

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.composition.ModelsComposition

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in interface org.apache.ignite.ml.environment.deploy.DeployableObject

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.developer.MappedPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.developer.PatchedPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.encoding.onehotencoder.OneHotEncoderPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDependencies() - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerPreprocessor

Returns dependencies of this object that can be object with class defined by client side and unknown for server.

getDepth() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getDesc() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

getDistance() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModelFormat

getDistance() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Gets the distance.

getDistance() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Gets the distance.

getDistance() - Method in class org.apache.ignite.ml.knn.utils.PointWithDistance

getDistanceMeasure() - Method in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Gets distance measure.

getDistanceMeasure() - Method in class org.apache.ignite.ml.knn.NNClassificationModel

getDistances(Vector, LabeledVectorSet<LabeledVector<Double>>) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Computes distances between given vector and each vector in training dataset.

getDistanceSquared(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Get the square of the distance between this vector and the argument vector.

getDistanceSquared(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Get the square of the distance between this vector and the argument vector.

getDistanceSquared(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Get the square of the distance between this vector and the argument vector.

getElement(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the matrix's element at the given coordinates.

getElement(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the matrix's element at the given coordinates.

getElement(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets element at the given index.

getElement(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets element at the given index.

getElement(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets element at the given index.

getElitismCnt() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the elitism count

getElseNode() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

getEpsilon() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Gets the epsilon.

getEpsilon() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Gets the epsilon.

getFeatureId() - Method in class org.apache.ignite.ml.dataset.feature.FeatureMeta

getFeatureMeta() - Method in class org.apache.ignite.ml.dataset.feature.BucketMeta

getFeatureName(int) - Method in class org.apache.ignite.ml.structures.Dataset

Returns feature name for column with given index.

getFeatures() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleDatasetData

getFeatures() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData

getFeatures() - Method in class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData

getFeaturesMapping() - Method in class org.apache.ignite.ml.composition.ModelOnFeaturesSubspace

Returns features mapping.

getFeatureValue(DecisionTreeData, TreeDataIndex, int, int) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Returns feature value in according to kth order statistic.

getFeatureValues(DecisionTreeData, TreeDataIndex, int, int) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Returns feature value in according to kth order statistic.

getFile(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns file content.

getFile(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns file content.

getFileName() - Method in enum org.apache.ignite.ml.util.MLSandboxDatasets

getFileStat(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns statistics for file.

getFileStat(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns statistics for file.

getFitness() - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Returns the fitness value.

getFitnessFunction() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve IFitnessFunction

getFitnessScore() - Method in class org.apache.ignite.ml.genetic.Chromosome

Gets the fitnessScore

getGain() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

getGene(int) - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Returns the gene value by index.

getGenePool() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the gene pool

getGenes() - Method in class org.apache.ignite.ml.genetic.Chromosome

Gets the gene keys (ie: primary keys) for this chromosome

getGenesInOrderForChromosome(Ignite, Chromosome) - Static method in class org.apache.ignite.ml.genetic.utils.GAGridUtils

Retrieve genes in order

getHyperParameterTuningStrategy() - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Returns the Hyper-parameter tuning strategy.

getId() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getImpurity() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

getImpurity() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getImpurityMeasureCalculator(Dataset<EmptyContext, DecisionTreeData>) - Method in class org.apache.ignite.ml.tree.DecisionTree

Returns impurity measure calculator.

getImpurityMeasureCalculator(Dataset<EmptyContext, DecisionTreeData>) - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Returns impurity measure calculator.

getImpurityMeasureCalculator(Dataset<EmptyContext, DecisionTreeData>) - Method in class org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer

Returns impurity measure calculator.

getInputMsg() - Method in class org.apache.ignite.ml.inference.ModelSignature

getInt() - Method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

getIntercept() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

getInternalMdl() - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

getIsstop() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getIterations() - Method in class org.apache.ignite.ml.math.isolve.IterativeSolverResult

getK() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Gets the amount of clusters.

getK() - Method in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Gets amount of nearest neighbors.

getKClosestVectors(LabeledVectorSet<LabeledVector>, TreeMap<Double, Set<Integer>>) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Iterates along entries in distance map and fill the resulting k-element array.

getKey() - Method in class org.apache.ignite.ml.dataset.UpstreamEntry

getKeys() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

Returns list of string indexes used in this vector.

getKeys() - Method in interface org.apache.ignite.ml.math.primitives.vector.NamedVector

Returns list of string indexes used in this vector.

getL() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Returns the matrix L of the decomposition.

getLabel() - Method in class org.apache.ignite.ml.util.MnistUtils.MnistLabeledImage

getLabels() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData

getLabels() - Method in class org.apache.ignite.ml.dataset.primitive.FeatureMatrixWithLabelsOnHeapData

getLabelValue(DecisionTreeData, TreeDataIndex, int, int) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Returns label value in according to kth order statistic.

getLambda() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Get the regularization lambda.

getLastTrainedModelOrThrowEmptyDatasetException(M) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Used on update phase when given dataset is empty.

getLeafs() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeRoot

getLearningEnvironment(Ignite, UUID, int, LearningEnvironmentBuilder) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Gets learning environment for given partition.

getLearningStrategy() - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Returns learning strategy.

getLearningStrategy() - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Returns learning strategy.

getLearningStrategy() - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Returns learning strategy.

getLeft() - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasure

getLeft() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getLeftCnt() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getLeftY() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getLeftY2() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getLengthSquared() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the sum of squares of all elements in this vector.

getLengthSquared() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the sum of squares of all elements in this vector.

getLengthSquared() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the sum of squares of all elements in this vector.

getLocIterations() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the maximal number of local iterations before synchronization.

getLocIterations() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Get the amount of local iterations.

getLocIterations() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Get the amount of local iterations.

getLoss() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the loss function to be minimized during the training.

getMapping(int, int, long) - Static method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

Get mapping R^featuresVectorSize -> R^maximumFeaturesCntPerMdl.

getMapping(int, int, long) - Static method in class org.apache.ignite.ml.trainers.TrainerTransformers

Get mapping R^featuresVectorSize -> R^maximumFeaturesCntPerMdl.

getMax() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPartitionData

getMax() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor

getMaxAbs() - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPartitionData

getMaxAbs() - Method in class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPreprocessor

getMaxDeep() - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

getMaxDepth() - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Get the max depth.

getMaxDepth() - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Get the max depth.

getMaxIterations() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Gets the max number of iterations before convergence.

getMaxIterations() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Gets the max number of iterations before convergence.

getMaxIterations() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the maximal number of iterations before the training will be stopped.

getMaxIterations() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Get the max amount of iterations.

getMaxIterations() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Get the max amount of iterations.

getMaxTries() - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Returns the max number of tries to stop the hyperparameter search.

getMdl() - Method in class org.apache.ignite.ml.composition.ModelOnFeaturesSubspace

Returns model.

getMdlDescStorageBackups() - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

getMdlStorageBackups() - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

getMeans() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

getMeans() - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerPreprocessor

getMeanValue() - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

getMetaStorage() - Method in interface org.apache.ignite.ml.math.MetaAttributes

Implementation should return an instance of the map to store meta attributes.

getMetaStorage() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Implementation should return an instance of the map to store meta attributes.

getMetaStorage() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Implementation should return an instance of the map to store meta attributes.

getMetaStorage() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Implementation should return an instance of the map to store meta attributes.

getMin() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPartitionData

getMin() - Method in class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor

getMinImpurityDecrease() - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Get the min impurity decrease.

getMinImpurityDecrease() - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Get the min impurity decrease.

getMinImpurityDecrease() - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

getMissingNode() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

getModel(Ignite, String) - Static method in class org.apache.ignite.ml.inference.IgniteModelStorageUtil

Retrieves Ignite model by name using SingleModelBuilder.

getModel(Double) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

getModelDescriptorStorage(Ignite) - Method in class org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorageFactory

Returns model descriptor storage based on Apache Ignite cache.

getModels() - Method in class org.apache.ignite.ml.composition.ModelsComposition

Returns containing models.

getModelStorage(Ignite) - Method in class org.apache.ignite.ml.inference.storage.model.ModelStorageFactory

Returns model storage based on Apache Ignite cache.

getModificationTime() - Method in class org.apache.ignite.ml.inference.storage.model.FileStat

getModificationTs() - Method in interface org.apache.ignite.ml.inference.storage.model.FileOrDirectory

Returns time since file modification.

getMutationProbability() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getMutationRate() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the mutation rate.

getName() - Method in class org.apache.ignite.ml.dataset.feature.FeatureMeta

getName() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

getName() - Method in class org.apache.ignite.ml.selection.paramgrid.BruteForceStrategy

Returns the name of strategy.

getName() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

Returns the name of strategy.

getName() - Method in class org.apache.ignite.ml.selection.paramgrid.HyperParameterTuningStrategy

Returns the name of strategy.

getName() - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Returns the name of strategy.

getNodeId() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer.NodeImpurityHistograms

getObj() - Method in class org.apache.ignite.ml.recommendation.ObjectSubjectPair

Returns object of recommendation.

getObjects() - Method in class org.apache.ignite.ml.recommendation.util.RecommendationDatasetData

Returns set of objects contained in RecommendationDatasetData.ratings.

getObjGrad() - Method in class org.apache.ignite.ml.recommendation.util.MatrixFactorizationGradient

Returns gradient of object of recommendation matrix (unmodifiable).

getOpt() - Method in interface org.apache.ignite.ml.environment.parallelism.Promise

Wrap result of Future to Optional-object.

getOutputMsg() - Method in class org.apache.ignite.ml.inference.ModelSignature

getP() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Returns the P rows permutation matrix.

getParallelism() - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy

Returns default parallelism.

getParallelism() - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy

Returns default parallelism.

getParallelism() - Method in interface org.apache.ignite.ml.environment.parallelism.ParallelismStrategy

Returns default parallelism.

getParamNameByIndex(int) - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Returns the name of hyper-parameter by the given index.

getParamRawData() - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Prepare data for hyper-parameter tuning.

getParamValuesByParamIdx() - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

getParser() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

getPivot() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Returns the pivot permutation vector.

getPixels() - Method in class org.apache.ignite.ml.util.MnistUtils.MnistImage

getPnt() - Method in class org.apache.ignite.ml.knn.utils.PointWithDistance

getPopulationSize() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve the population size

getPrediction() - Method in class org.apache.ignite.ml.selection.scoring.LabelPair

getPredictionsAggregator() - Method in class org.apache.ignite.ml.composition.ModelsComposition

Returns predictions aggregator.

getPreprocessor() - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

getProbabilities() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

getProjector(int[]) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Get projector from index mapping.

getR1norm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getR2norm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getRating() - Method in class org.apache.ignite.ml.recommendation.ObjectSubjectRatingTriplet

Returns rating.

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the value at specified index.

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the value at specified index.

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

getRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

getRaw() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector.Element

Gets element's value.

getRaw(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the value at specified index.

getRaw(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

getRaw(int) - Method in class org.apache.ignite.ml.structures.DatasetRow

Gets the value at specified index.

getRawX(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the value at specified index without checking for index boundaries.

getRawX(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the value at specified index without checking for index boundaries.

getRawX(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the value at specified index without checking for index boundaries.

getReader() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

getRight() - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasure

getRight() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getRightCnt() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getRightY() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getRightY2() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

getRootNode() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeRoot

getRow(int) - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

Returns vector from dataset in according to row id.

getRow(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Get a specific row from matrix.

getRow(int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Get a specific row from matrix.

getRow(int) - Method in class org.apache.ignite.ml.structures.Dataset

Retrieves Labeled Vector by given index.

getRows() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleDatasetData

getRows() - Method in class org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData

getRows() - Method in class org.apache.ignite.ml.recommendation.util.MatrixFactorizationGradient

Returns number of rows the gradient calculated on.

getRowsCount() - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

Returns rows count.

getSatisfactoryFitness() - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

getSchema() - Method in class org.apache.ignite.ml.inference.ModelSignature

getScoringBoard() - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Gets the Scoring Board.

getSeed() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the multilayer perceptron model initializer.

getSeed() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Get the seed for random generator.

getSeed() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Get the seed for random generator.

getSeed() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getSeed() - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Returns the seed.

getSeed() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Get the seed number.

getSelectionMtd() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Get the selection method

getSelectionStgy() - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

getSeparator() - Method in enum org.apache.ignite.ml.util.MLSandboxDatasets

getSetterByIndex(int) - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Returns setter for parameter with the given index.

getSigmas() - Method in class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerPreprocessor

getSignature() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

getSize() - Method in class org.apache.ignite.ml.inference.storage.model.FileStat

getSolutionById(int) - Static method in class org.apache.ignite.ml.genetic.functions.GAGridFunction

Retrieve and individual solution by Chromosome key.

getSolutionsAsc() - Static method in class org.apache.ignite.ml.genetic.functions.GAGridFunction

Retrieve solutions in ascending order based on fitness score.

getSolutionsDesc() - Static method in class org.apache.ignite.ml.genetic.functions.GAGridFunction

Retrieve solutions in descending order based on fitness score.

getStorage() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets matrix storage model.

getStorage() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets matrix storage model.

getStorage() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets vector storage model.

getStorage() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets vector storage model.

getStorage() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets vector storage model.

getSubj() - Method in class org.apache.ignite.ml.recommendation.ObjectSubjectPair

Returns subject of recommendation.

getSubjects() - Method in class org.apache.ignite.ml.recommendation.util.RecommendationDatasetData

Returns set of subjects contained in RecommendationDatasetData.ratings.

getSubjGrad() - Method in class org.apache.ignite.ml.recommendation.util.MatrixFactorizationGradient

Returns gradient of subject of recommendation function (unmodifiable).

getSumOfValues() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

getSumsHolder() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

getSumsHolder() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

getSyncModel(Ignite, String, SyncModelBuilder) - Static method in class org.apache.ignite.ml.inference.IgniteModelStorageUtil

Retrieves Ignite model by name using synchronous model builder.

getTerminateCriteria() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retreive the termination criteria

getTestFilter() - Method in class org.apache.ignite.ml.selection.split.TrainTestSplit

getTheBestSolution() - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

Returns the best chromosome's genes presented as double array.

getThenNode() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

getThreshold() - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationPreprocessor

Get the threshold parameter.

getThreshold() - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationTrainer

Get the threshold parameter value.

getThreshold() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

getTotalFitness() - Method in class org.apache.ignite.ml.util.genetic.Population

Returns the total fitness value of population or Double.NaN if not all fitnesses are calculated for all chromosomes.

getTrainer() - Method in class org.apache.ignite.ml.pipeline.Pipeline

Returns trainer.

getTrainFilter() - Method in class org.apache.ignite.ml.selection.split.TrainTestSplit

getTruncateRate() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Retrieve truncateRate

getTruth() - Method in class org.apache.ignite.ml.selection.scoring.LabelPair

getType() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

getU() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRPartitionContext

getU() - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Returns the matrix U of the decomposition.

getUpdatesCalculator() - Method in class org.apache.ignite.ml.nn.UpdatesStrategy

Get parameter update calculator (see ParameterUpdateCalculator).

getUpdatesStgy() - Method in class org.apache.ignite.ml.nn.MLPTrainer

Get the update strategy that defines how to update model parameters during the training.

getUpdatesStgy() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Get the update strategy.

getUpdatesStgy() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Get the update strategy.

getUpstreamCache() - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDataset

getUsedFeatures() - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeRoot

getVal() - Method in class org.apache.ignite.ml.genetic.Gene

getVal() - Method in class org.apache.ignite.ml.tree.DecisionTreeLeafNode

getVal() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

getValue(Integer) - Method in interface org.apache.ignite.ml.dataset.feature.Histogram

getValue(Integer) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

getValue() - Method in class org.apache.ignite.ml.dataset.UpstreamEntry

getValue(Integer) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

getValue(Integer) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

getVar() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getVariances() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

getVector() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Get the delegating vector

getWeights() - Method in class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

getWeights() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

getWithId() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily

getX() - Method in class org.apache.ignite.ml.math.isolve.IterativeSolverResult

getX(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the matrix value at the provided location without checking boundaries.

getX(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the matrix value at the provided location without checking boundaries.

getX(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the value at specified index without checking for index boundaries.

getX(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the value at specified index without checking for index boundaries.

getX(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the value at specified index without checking for index boundaries.

getX() - Method in class org.apache.ignite.ml.tree.impurity.util.StepFunction

getXnorm() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

getY() - Method in class org.apache.ignite.ml.structures.partition.LabelPartitionDataOnHeap

getY() - Method in class org.apache.ignite.ml.tree.impurity.util.StepFunction

GiniHistogram - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Class contains implementation of splitting point finding algorithm based on Gini metric (see https://en.wikipedia.org/wiki/Gini_coefficient) and represents a set of histograms in according to this metric.

GiniHistogram(int, Map<Double, Integer>, BucketMeta) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

Creates an instance of GiniHistogram.

GiniHistogramsComputer - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Implementation of ImpurityHistogramsComputer for classification task.

GiniHistogramsComputer(Map<Double, Integer>) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogramsComputer

Creates an instance of GiniHistogramsComputer.

GiniImpurityMeasure - Class in org.apache.ignite.ml.tree.impurity.gini

Gini impurity measure which is calculated the following way: \-frac{1}{L}\sum_{i=1}^{s}l_i^2 - \frac{1}{R}\sum_{i=s+1}^{n}r_i^2.

GiniImpurityMeasureCalculator - Class in org.apache.ignite.ml.tree.impurity.gini

Gini impurity measure calculator.

GiniImpurityMeasureCalculator(Map<Double, Integer>, boolean) - Constructor for class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasureCalculator

Constructs a new instance of Gini impurity measure calculator.

GmmModel - Class in org.apache.ignite.ml.clustering.gmm

Gaussian Mixture Model.

GmmModel(Vector, List<MultivariateGaussianDistribution>) - Constructor for class org.apache.ignite.ml.clustering.gmm.GmmModel

Creates an instance of GmmModel.

GmmTrainer - Class in org.apache.ignite.ml.clustering.gmm

Traner for GMM model.

GmmTrainer() - Constructor for class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Creates an instance of GmmTrainer.

GmmTrainer(int, int) - Constructor for class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Creates an instance of GmmTrainer.

GmmTrainer(int) - Constructor for class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Creates an instance of GmmTrainer.

gradient(long, double, double) - Method in class org.apache.ignite.ml.composition.boosting.loss.LogLoss

Error gradient value for model answer.

gradient(long, double, double) - Method in interface org.apache.ignite.ml.composition.boosting.loss.Loss

Error gradient value for model answer.

gradient(long, double, double) - Method in class org.apache.ignite.ml.composition.boosting.loss.SquaredError

Error gradient value for model answer.

gradient() - Method in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate

Get gradient.

guid() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Auto-generated globally unique matrix ID.

guid() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Auto-generated globally unique matrix ID.

guid() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Auto-generated globally unique vector ID.

guid() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Auto-generated globally unique vector ID.

guid() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Auto-generated globally unique vector ID.

H

HammingDistance - Class in org.apache.ignite.ml.math.distances

Calculates the Hamming distance between two points.

HammingDistance() - Constructor for class org.apache.ignite.ml.math.distances.HammingDistance

handledIndices - Variable in class org.apache.ignite.ml.preprocessing.encoding.EncoderPreprocessor

Feature indices to apply encoder.

hasAttribute(String) - Method in interface org.apache.ignite.ml.math.MetaAttributes

Checks if given meta attribute is present.

hasBias() - Method in class org.apache.ignite.ml.nn.architecture.TransformationLayerArchitecture

Checks if this layer has a bias.

hasBiases(int) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Checks if layer with given index has biases.

hashCode() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

hashCode() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModelFormat

hashCode() - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

hashCode() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

hashCode() - Method in class org.apache.ignite.ml.knn.ann.ANNModelFormat

hashCode() - Method in class org.apache.ignite.ml.knn.ann.KNNModelFormat

hashCode() - Method in class org.apache.ignite.ml.knn.NNClassificationModel

hashCode() - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

hashCode() - Method in class org.apache.ignite.ml.math.distances.HammingDistance

hashCode() - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

hashCode() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

hashCode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

hashCode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

hashCode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

hashCode() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

hashCode() - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

hashCode() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

hashCode() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

hashCode() - Method in class org.apache.ignite.ml.structures.Dataset

hashCode() - Method in class org.apache.ignite.ml.structures.DatasetRow

hashCode() - Method in class org.apache.ignite.ml.structures.FeatureMetadata

hashCode() - Method in class org.apache.ignite.ml.structures.LabeledVector

hashCode() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

hashCode() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeId

hasHeader() - Method in enum org.apache.ignite.ml.util.MLSandboxDatasets

hasNext() - Method in class org.apache.ignite.ml.dataset.impl.cache.util.IteratorWithConcurrentModificationChecker

HIGH - Static variable in class org.apache.ignite.ml.environment.logging.ConsoleLogger.Factory

High.

HINGE - Static variable in class org.apache.ignite.ml.optimization.LossFunctions

Hinge loss function.

Histogram<T,H extends Histogram<T,H>> - Interface in org.apache.ignite.ml.dataset.feature

Interface of histogram over type T.

HyperParameterTuningStrategy - Class in org.apache.ignite.ml.selection.paramgrid

The parent abstract class of all hyper-parameter tuning strategies.

HyperParameterTuningStrategy() - Constructor for class org.apache.ignite.ml.selection.paramgrid.HyperParameterTuningStrategy

I

id() - Method in class org.apache.ignite.ml.genetic.Chromosome

Get the id (primary key) for this chromosome

id() - Method in class org.apache.ignite.ml.genetic.Gene

id() - Method in class org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor

id() - Method in enum org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor.Method

identity() - Static method in interface org.apache.ignite.ml.dataset.UpstreamTransformerBuilder

Returns identity upstream transformer.

IDENTITY - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns its argument.

identity() - Static method in interface org.apache.ignite.ml.math.functions.IgniteFunction

Identity function.

identity(int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

identityLike(Matrix, int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the identity matrix like a given matrix.

identityTrainer() - Static method in class org.apache.ignite.ml.trainers.DatasetTrainer

Returns the trainer which returns identity model.

IFitnessFunction - Interface in org.apache.ignite.ml.genetic

Fitness function are used to determine how optimal a particular solution is relative to other solutions.

IgniteBiConsumer<T,U> - Interface in org.apache.ignite.ml.math.functions

Serializable binary consumer.

IgniteBiFunction<T,U,R> - Interface in org.apache.ignite.ml.math.functions

Serializable binary function.

IgniteBinaryOperator<A> - Interface in org.apache.ignite.ml.math.functions

Serializable binary operator.

IgniteConsumer<T> - Interface in org.apache.ignite.ml.math.functions

Serializable consumer.

IgniteCurriedBiFunction<A,B,T> - Interface in org.apache.ignite.ml.math.functions

Serializable binary function.

IgniteCurriedTriFunction<A,B,C,D> - Interface in org.apache.ignite.ml.math.functions

Serializable curried tri-function.

IgniteDifferentiableDoubleToDoubleFunction - Interface in org.apache.ignite.ml.math.functions

Interface for differentiable functions from double to double.

IgniteDifferentiableVectorToDoubleFunction - Interface in org.apache.ignite.ml.math.functions

Interface for differentiable functions from vector to double.

IgniteDistributedModelBuilder - Class in org.apache.ignite.ml.inference.builder

Builder that allows to start Apache Ignite services for distributed inference and get a facade that allows to work with this distributed inference infrastructure as with a single inference model (see Model).

IgniteDistributedModelBuilder(Ignite, int, int) - Constructor for class org.apache.ignite.ml.inference.builder.IgniteDistributedModelBuilder

Constructs a new instance of Ignite distributed inference model builder.

IgniteDoubleFunction<Double> - Interface in org.apache.ignite.ml.math.functions

Serializable double function.

IgniteFunction<T,R> - Interface in org.apache.ignite.ml.math.functions

Serializable function.

IgniteIntDoubleToDoubleBiFunction - Interface in org.apache.ignite.ml.math.functions

BiFunction (int, double) -> double.

IgniteIntIntToIntBiFunction - Interface in org.apache.ignite.ml.math.functions

BiFunction (int, int) -> int.

IgniteModel<T,V> - Interface in org.apache.ignite.ml

Basic interface for all models.

IgniteModelDescriptorStorage - Class in org.apache.ignite.ml.inference.storage.descriptor

Model descriptor storage based on Apache Ignite cache.

IgniteModelDescriptorStorage(IgniteCache<String, ModelDescriptor>) - Constructor for class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Constructs a new instance of Ignite model descriptor storage.

IgniteModelParser<I,O> - Class in org.apache.ignite.ml.inference.parser

Implementation of model parser that accepts serialized IgniteFunction.

IgniteModelParser() - Constructor for class org.apache.ignite.ml.inference.parser.IgniteModelParser

IgniteModelStorageProvider - Class in org.apache.ignite.ml.inference.storage.model

Implementation of ModelStorageProvider based on Apache Ignite cache.

IgniteModelStorageProvider(IgniteCache<String, FileOrDirectory>) - Constructor for class org.apache.ignite.ml.inference.storage.model.IgniteModelStorageProvider

Constructs a new instance of Ignite model storage provider.

IgniteModelStorageUtil - Class in org.apache.ignite.ml.inference

Utils class that helps to operate with model storage and Ignite models.

IgniteModelStorageUtil() - Constructor for class org.apache.ignite.ml.inference.IgniteModelStorageUtil

IgniteSupplier<T> - Interface in org.apache.ignite.ml.math.functions

Serializable supplier.

IgniteToDoubleFunction<T> - Interface in org.apache.ignite.ml.math.functions

Serializable function that produces a double-valued result.

IgniteTriConsumer<A,B,C> - Interface in org.apache.ignite.ml.math.functions

Serializable tri-consumer.

IgniteTriFunction<A,B,C,R> - Interface in org.apache.ignite.ml.math.functions

Serializable TriFunction (A, B, C) -> R.

impurity() - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasure

Calculates impurity measure as a single double value.

impurity() - Method in interface org.apache.ignite.ml.tree.impurity.ImpurityMeasure

Calculates impurity measure as a single double value.

impurity() - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

Calculates impurity measure as a single double value.

ImpurityComputer<T,H extends Histogram<T,H>> - Interface in org.apache.ignite.ml.tree.randomforest.data.impurity

Interface represents an object that can compute best splitting point using features histograms.

ImpurityHistogram - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Helper class for ImpurityHistograms.

ImpurityHistogram(int) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogram

Creates an instance of ImpurityHistogram.

ImpurityHistogramsComputer<S extends ImpurityComputer<BootstrappedVector,S>> - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Class containing logic of aggregation impurity statistics within learning dataset.

ImpurityHistogramsComputer() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer

ImpurityHistogramsComputer.NodeImpurityHistograms<S extends ImpurityComputer<BootstrappedVector,S>> - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Class represents per feature statistics for impurity computing.

ImpurityMeasure<T extends ImpurityMeasure<T>> - Interface in org.apache.ignite.ml.tree.impurity

Base interface for impurity measures that can be used in distributed decision tree algorithm.

ImpurityMeasureCalculator<T extends ImpurityMeasure<T>> - Class in org.apache.ignite.ml.tree.impurity

Base interface for impurity measure calculators that calculates all impurity measures required to find a best split.

ImpurityMeasureCalculator(boolean) - Constructor for class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Constructs an instance of ImpurityMeasureCalculator.

ImputerPartitionData - Class in org.apache.ignite.ml.preprocessing.imputing

Partition data used in imputing preprocessor.

ImputerPartitionData() - Constructor for class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Constructs a new instance of imputing partition data.

ImputerPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.imputing

Preprocessing function that makes imputing.

ImputerPreprocessor(Vector, Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.imputing.ImputerPreprocessor

Constructs a new instance of imputing preprocessor.

ImputerTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.imputing

Trainer of the imputing preprocessor.

ImputerTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer

ImputingStrategy - Enum in org.apache.ignite.ml.preprocessing.imputing

This enum contains settings for imputing preprocessor.

increment(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Increments value at given index.

increment(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Increments value at given index.

increment(int, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Increments value at given index.

incrementX(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Increments value at given index without checking for index boundaries.

incrementX(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Increments value at given index without checking for index boundaries.

incrementX(int, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Increments value at given index without checking for index boundaries.

index() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector.Element

Gets element's index in the vector.

indexes() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

Indexes of non-default elements.

indexes() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

indexesMap() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

indexesMap() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

IndexException - Exception in org.apache.ignite.ml.math.exceptions

Indicates an invalid, i.e. out of bound, index on matrix or vector operations.

IndexException(int) - Constructor for exception org.apache.ignite.ml.math.exceptions.IndexException

init(DeployingContext) - Method in interface org.apache.ignite.ml.environment.deploy.DeployingContext

Inits context by other context.

init(DeployingContext) - Method in class org.apache.ignite.ml.environment.deploy.DeployingContextImpl

Inits context by other context.

init(M, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>) - Method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovUpdateCalculator

Initializes the update calculator.

init(M, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>) - Method in interface org.apache.ignite.ml.optimization.updatecalculators.ParameterUpdateCalculator

Initializes the update calculator.

init(SmoothParametrized, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>) - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Initializes the update calculator.

init(SmoothParametrized, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>) - Method in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Initializes the update calculator.

init(Dataset<EmptyContext, BootstrappedDatasetPartition>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Aggregates all unique labels from dataset and assigns integer id value for each label.

init(Dataset<EmptyContext, BootstrappedDatasetPartition>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Init-step before learning.

initBiases(Vector) - Method in interface org.apache.ignite.ml.nn.initializers.MLPInitializer

In-place change values of vector representing vectors.

initBiases(Vector) - Method in class org.apache.ignite.ml.nn.initializers.RandomInitializer

In-place change values of vector representing vectors.

initByClientObject(Object) - Method in interface org.apache.ignite.ml.environment.deploy.DeployingContext

Inits context by any client-defined object.

initByClientObject(Object) - Method in class org.apache.ignite.ml.environment.deploy.DeployingContextImpl

Inits context by any client-defined object.

initContext(Ignite, String, UpstreamTransformerBuilder, IgniteBiPredicate<K, V>, String, PartitionContextBuilder<K, V, C>, LearningEnvironmentBuilder, int, int, boolean, DeployingContext) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Initializes partition context by loading it from a partition upstream.

initDeployingContext(Object) - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Initializes deploying context by object representing current client computation with classes unknown for server side.

initExtensions(PluginContext, ExtensionRegistry) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

initLearningState(GDBTrainer.GDBModel) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Restores state of already learned model if can and sets learning parameters according to this state.

initTrees(Queue<TreeNode>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Creates list of trees.

initWeights(Matrix) - Method in interface org.apache.ignite.ml.nn.initializers.MLPInitializer

In-place change values of matrix representing weights.

initWeights(Matrix) - Method in class org.apache.ignite.ml.nn.initializers.RandomInitializer

In-place change values of matrix representing weights.

InMemoryModelReader - Class in org.apache.ignite.ml.inference.reader

Model reader that reads predefined array of bytes.

InMemoryModelReader(byte[]) - Constructor for class org.apache.ignite.ml.inference.reader.InMemoryModelReader

Constructs a new instance of in-memory model reader that returns specified byte array.

InMemoryModelReader(T) - Constructor for class org.apache.ignite.ml.inference.reader.InMemoryModelReader

Constructs a new instance of in-memory model reader that returns serialized specified object.

innerModel() - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Get inner model.

input - Variable in class org.apache.ignite.ml.nn.MLPState

Input.

inputSize() - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Size of input of MLP.

INSTANCE - Static variable in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy

Instance.

instance() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

instance() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer

instance() - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

IntCoordVectorizer(Integer...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.IntCoordVectorizer

Creates an instance of Vectorizer.

IntDoubleToVoidFunction - Interface in org.apache.ignite.ml.math.functions

Setter function for the vector.

intercept() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Gets the intercept.

intercept() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Gets the intercept.

internalLabelToExternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Maps internal representation of label to external.

internalLabelToExternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBRegressionTrainer

Maps internal representation of label to external.

internalLabelToExternal(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Maps internal representation of label to external.

IntIntDoubleToVoidFunction - Interface in org.apache.ignite.ml.math.functions

Setter function for matrices.

IntIntToDoubleFunction - Interface in org.apache.ignite.ml.math.functions

Getters functions for matrices.

INV - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns 1 / a

inverse() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Returns the inverse matrix of this matrix

inverse() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Returns the inverse matrix of this matrix

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

Checks if implementation is based on Java arrays.

isArrayBased() - Method in interface org.apache.ignite.ml.math.StorageOpsMetrics

Checks if implementation is based on Java arrays.

isCancelled() - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

isCancelled() - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

isCategoricalFeature() - Method in class org.apache.ignite.ml.dataset.feature.FeatureMeta

isConverged(LearningEnvironmentBuilder, DatasetBuilder<K, V>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

Checks convergency on dataset.

isConverged(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceChecker

Checks convergency on dataset.

isConverged(LearningEnvironmentBuilder, DatasetBuilder<K, V>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub

Checks convergency on dataset.

isConverged(Dataset<EmptyContext, ? extends FeatureMatrixWithLabelsOnHeapData>, ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.convergence.simple.ConvergenceCheckerStub

Checks convergency on dataset.

isDense() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDense() - Method in interface org.apache.ignite.ml.math.StorageOpsMetrics

Checks if this implementation should be considered dense so that it explicitly represents every value.

isDirectory(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns true if the specified path associated with a directory.

isDirectory() - Method in interface org.apache.ignite.ml.inference.storage.model.FileOrDirectory

Return true if this object is a directory, otherwise false.

isDirectory() - Method in class org.apache.ignite.ml.inference.storage.model.FileStat

isDirectory(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns true if the specified path associated with a directory.

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed() - Method in interface org.apache.ignite.ml.math.StorageOpsMetrics

Checks whether implementation is JVM-local or distributed (multi-JVM).

isDistributed - Variable in class org.apache.ignite.ml.structures.Dataset

isDistributed() - Method in class org.apache.ignite.ml.structures.Dataset

isDone() - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy.FutureWrapper

isDone() - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

isEqualTo(H) - Method in interface org.apache.ignite.ml.dataset.feature.Histogram

Compares histogram with other and returns true if they are equals

isEqualTo(ObjectHistogram<T>) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Compares histogram with other and returns true if they are equals

isEqualTo(GiniHistogram) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

Compares histogram with other and returns true if they are equals

isEqualTo(MSEHistogram) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

Compares histogram with other and returns true if they are equals

isFile(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns true if the specified path associated with a regular file.

isFile() - Method in interface org.apache.ignite.ml.inference.storage.model.FileOrDirectory

Returns true if this object is a regular file, otherwise false.

isFile(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns true if the specified path associated with a regular file.

isHigherFitnessValFitter() - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

isKeepingRawLabels() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Gets the output label format mode.

isKeepingRawLabels() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Gets the output label format mode.

isNumeric() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

isNumeric() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

isNumeric() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

isNumeric() - Method in interface org.apache.ignite.ml.math.StorageOpsMetrics

isROCAUCenabled() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

isRunningOnPipeline - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Execution over the pipeline or the chain of preprocessors and separate trainer, otherwise.

isRunningOnPipeline(boolean) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

isTerminationConditionMet(Chromosome, double, int) - Method in interface org.apache.ignite.ml.genetic.parameter.ITerminateCriteria

isUpdateable(GmmModel) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

isUpdateable(KMeansModel) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

isUpdateable(BaggedModel) - Method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

isUpdateable(ModelsComposition) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

isUpdateable(IgniteModel<I, List<O>>) - Method in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

isUpdateable(ModelsSequentialComposition<I, O1, O2>) - Method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

isUpdateable(StackedModel<IS, IA, O, AM>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

isUpdateable(ANNClassificationModel) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

isUpdateable(M) - Method in class org.apache.ignite.ml.knn.KNNTrainer

isUpdateable(MultiClassModel<M>) - Method in class org.apache.ignite.ml.multiclass.OneVsRestTrainer

isUpdateable(DiscreteNaiveBayesModel) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

isUpdateable(GaussianNaiveBayesModel) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

isUpdateable(MultilayerPerceptron) - Method in class org.apache.ignite.ml.nn.MLPTrainer

isUpdateable(LinearRegressionModel) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer

isUpdateable(LinearRegressionModel) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

isUpdateable(LogisticRegressionModel) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

isUpdateable(SVMLinearClassificationModel) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

isUpdateable(AdaptableDatasetModel<I, O, IW, OW, M>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

isUpdateable(M) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

isUpdateable(DecisionTreeNode) - Method in class org.apache.ignite.ml.tree.DecisionTree

isUpdateable(ModelsComposition) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

isUsingIdx() - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Get the using index structure property instead of using sorting during the learning process.

isUsingIdx() - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Get the using index structure property instead of using sorting during the learning process.

isWeighted() - Method in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Weighted or not.

isWithMdlDescStorage() - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

isWithMdlStorage() - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

isZero(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Tests if given value is considered a zero value.

iter(double, double[]) - Method in class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

Perform LSQR iteration.

iter(double, double[]) - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

Perform LSQR iteration.

IterativeSolverResult - Class in org.apache.ignite.ml.math.isolve

Base class for iterative solver results.

IterativeSolverResult(double[], int) - Constructor for class org.apache.ignite.ml.math.isolve.IterativeSolverResult

Constructs a new instance of iterative solver result.

iterator() - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedDatasetPartition

iterator() - Method in class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Returns iterator of model descriptors stored in this model descriptor storage.

iterator() - Method in class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

Returns iterator of model descriptors stored in this model descriptor storage.

iterator() - Method in interface org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorage

Returns iterator of model descriptors stored in this model descriptor storage.

iterator() - Method in class org.apache.ignite.ml.selection.scoring.cursor.CacheBasedLabelPairCursor

iterator() - Method in class org.apache.ignite.ml.selection.scoring.cursor.LocalLabelPairCursor

IteratorWithConcurrentModificationChecker<T> - Class in org.apache.ignite.ml.dataset.impl.cache.util

Iterator wrapper that checks if number of entries in iterator is equal to expected.

IteratorWithConcurrentModificationChecker(Iterator<T>, long, String) - Constructor for class org.apache.ignite.ml.dataset.impl.cache.util.IteratorWithConcurrentModificationChecker

Constructs a new instance of iterator checked wrapper.

ITerminateCriteria - Interface in org.apache.ignite.ml.genetic.parameter

Represents the terminate condition for a genetic algorithm.

K

k - Variable in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Amount of nearest neighbors.

k - Variable in class org.apache.ignite.ml.knn.KNNModel

Number of neighbours.

k - Variable in class org.apache.ignite.ml.knn.KNNTrainer

Number of neighbours.

k - Variable in class org.apache.ignite.ml.knn.NNClassificationModel

Amount of nearest neighbors.

KDTreeSpatialIndex<L> - Class in org.apache.ignite.ml.knn.utils.indices

KD tree based implementation of SpatialIndex.

KDTreeSpatialIndex(List<LabeledVector<L>>, DistanceMeasure) - Constructor for class org.apache.ignite.ml.knn.utils.indices.KDTreeSpatialIndex

Constructs a new instance of KD tree spatial index.

KEY_FOR_NULL_VALUES - Static variable in class org.apache.ignite.ml.preprocessing.encoding.EncoderPreprocessor

KMeansModel - Class in org.apache.ignite.ml.clustering.kmeans

This class encapsulates result of clusterization by KMeans algorithm.

KMeansModel(Vector[], DistanceMeasure) - Constructor for class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Construct KMeans model with given centers and distanceMeasure measure.

KMeansModelFormat - Class in org.apache.ignite.ml.clustering.kmeans

K-means model representation.

KMeansModelFormat(Vector[], DistanceMeasure) - Constructor for class org.apache.ignite.ml.clustering.kmeans.KMeansModelFormat

KMeansTrainer - Class in org.apache.ignite.ml.clustering.kmeans

The trainer for KMeans algorithm.

KMeansTrainer() - Constructor for class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

KMeansTrainer.TotalCostAndCounts - Class in org.apache.ignite.ml.clustering.kmeans

Service class used for statistics.

KNNClassificationModel - Class in org.apache.ignite.ml.knn.classification

KNN classification model.

KNNClassificationTrainer - Class in org.apache.ignite.ml.knn.classification

KNN classification model trader that trains model on top of distribtued spatial indices.

KNNClassificationTrainer() - Constructor for class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer

KNNModel<L> - Class in org.apache.ignite.ml.knn

KNN model build on top of distribtued spatial indices.

KNNModel(Dataset<EmptyContext, SpatialIndex<L>>, DistanceMeasure, int, boolean) - Constructor for class org.apache.ignite.ml.knn.KNNModel

Constructs a new instance of KNN model.

KNNModelFormat - Class in org.apache.ignite.ml.knn.ann

kNN model representation.

KNNModelFormat() - Constructor for class org.apache.ignite.ml.knn.ann.KNNModelFormat

KNNModelFormat(int, DistanceMeasure, boolean) - Constructor for class org.apache.ignite.ml.knn.ann.KNNModelFormat

Creates an instance.

KNNPartitionDataBuilder<K,V> - Class in org.apache.ignite.ml.knn

Partition data builder for KNN algorithms based on SpatialIndex.

KNNPartitionDataBuilder(Preprocessor<K, V>, SpatialIndexType, DistanceMeasure) - Constructor for class org.apache.ignite.ml.knn.KNNPartitionDataBuilder

Constructs a new instance of KNN partition data builder.

KNNRegressionModel - Class in org.apache.ignite.ml.knn.regression

KNN regression model.

KNNRegressionTrainer - Class in org.apache.ignite.ml.knn.regression

KNN regression model trader that trains model on top of distribtued spatial indices.

KNNRegressionTrainer() - Constructor for class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer

KNNTrainer<M extends KNNModel<Double>,Self extends KNNTrainer<M,Self>> - Class in org.apache.ignite.ml.knn

KNN model trader that trains model on top of distribtued spatial indices.

KNNTrainer() - Constructor for class org.apache.ignite.ml.knn.KNNTrainer

KNNUtils - Class in org.apache.ignite.ml.knn.utils

Helper class for KNNRegression.

KNNUtils() - Constructor for class org.apache.ignite.ml.knn.utils.KNNUtils

kNorm(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the k-norm of the vector.

kNorm(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the k-norm of the vector.

kNorm(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the k-norm of the vector.

L

L1 - Static variable in class org.apache.ignite.ml.optimization.LossFunctions

L1 loss function.

L2 - Static variable in class org.apache.ignite.ml.optimization.LossFunctions

L2 loss function.

label(C, K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.DefaultLabelVectorizer

Extract label value by given coordinate.

label(Integer, K, LabeledVector<L>) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Extract label value by given coordinate.

label(C, K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Extract label value by given coordinate.

label(C, K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.VectorizerAdapter

Extract label value by given coordinate.

label() - Method in class org.apache.ignite.ml.structures.LabeledVector

Get the label.

label(int) - Method in class org.apache.ignite.ml.structures.LabeledVectorSet

Returns label if label is attached or null if label is missed.

labeled(C) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Sets label coordinate for Vectorizer.

labeled(Vectorizer.LabelCoordinate) - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Sets label coordinate for Vectorizer.

labeled(L) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates LabeledVector instance.

labeled() - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

labeled(IgniteFunction<Vector, Double>) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

labeled() - Method in class org.apache.ignite.ml.util.generators.standard.GaussianMixtureDataStream

labeled() - Method in class org.apache.ignite.ml.util.generators.standard.RegressionDataStream

labeled() - Method in class org.apache.ignite.ml.util.generators.standard.RingsDataStream

labeled() - Method in class org.apache.ignite.ml.util.generators.standard.TwoSeparableClassesDataStream

LabeledDatasetLoader - Class in org.apache.ignite.ml.structures.preprocessing

Data pre-processing step which loads data from different file types.

LabeledDatasetLoader() - Constructor for class org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader

LabeledDatasetPartitionDataBuilderOnHeap<K,V,C extends Serializable> - Class in org.apache.ignite.ml.structures.partition

Partition data builder that builds LabeledVectorSet.

LabeledDatasetPartitionDataBuilderOnHeap(Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.structures.partition.LabeledDatasetPartitionDataBuilderOnHeap

Constructs a new instance of SVM partition data builder.

LabeledDummyVectorizer<K,L> - Class in org.apache.ignite.ml.dataset.feature.extractor.impl

Vectorizer on LabeledVector.

LabeledDummyVectorizer(Integer...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Creates an instance of Vectorizer.

LabeledVector<L> - Class in org.apache.ignite.ml.structures

Class for vector with label.

LabeledVector() - Constructor for class org.apache.ignite.ml.structures.LabeledVector

Default constructor.

LabeledVector(Vector, L) - Constructor for class org.apache.ignite.ml.structures.LabeledVector

Construct labeled vector.

LabeledVectorSet<Row extends LabeledVector> - Class in org.apache.ignite.ml.structures

The set of labeled vectors used in local partition calculations.

LabeledVectorSet() - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Default constructor (required by Externalizable).

LabeledVectorSet(int, int, boolean) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new Labeled Dataset and initialized with empty data structure.

LabeledVectorSet(int, int) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new local Labeled Dataset and initialized with empty data structure.

LabeledVectorSet(int, int, String[], boolean) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new Labeled Dataset and initialized with empty data structure.

LabeledVectorSet(Row[]) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new Labeled Dataset by given data.

LabeledVectorSet(Row[], int) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new Labeled Dataset by given data.

LabeledVectorSet(double[][], double[]) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new local Labeled Dataset by matrix and vector of labels.

LabeledVectorSet(double[][], double[], String[], boolean) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSet

Creates new Labeled Dataset by matrix and vector of labels.

LabeledVectorSetTestTrainPair - Class in org.apache.ignite.ml.structures

Class for splitting Labeled Dataset on train and test sets.

LabeledVectorSetTestTrainPair(LabeledVectorSet, double) - Constructor for class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair

Creates two subsets of given dataset.

labelInSortedOrder(int, int) - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

Returns label for kth order statistic for target feature.

LabelPair<L> - Class in org.apache.ignite.ml.selection.scoring

Pair of truth value and predicated by model.

LabelPair(L, L) - Constructor for class org.apache.ignite.ml.selection.scoring.LabelPair

Constructs a new instance of truth with prediction.

LabelPairCursor<L> - Interface in org.apache.ignite.ml.selection.scoring.cursor

Closeable iterable that supplies pairs of truth and predictions (abstraction that hides a difference between querying data from Ignite cache and from local Map).

LabelPartitionDataBuilderOnHeap<K,V,C extends Serializable> - Class in org.apache.ignite.ml.structures.partition

Partition data builder that builds LabelPartitionDataOnHeap.

LabelPartitionDataBuilderOnHeap(Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.structures.partition.LabelPartitionDataBuilderOnHeap

Constructs a new instance of Label partition data builder.

LabelPartitionDataOnHeap - Class in org.apache.ignite.ml.structures.partition

On Heap partition data that keeps part of a labels.

LabelPartitionDataOnHeap(double[]) - Constructor for class org.apache.ignite.ml.structures.partition.LabelPartitionDataOnHeap

Constructs a new instance of linear system partition data.

labels() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer.CentroidStat

labels() - Method in class org.apache.ignite.ml.structures.LabeledVectorSet

Returns new copy of labels of all labeled vectors NOTE: This method is useful for copying labels from test dataset.

LayerArchitecture - Class in org.apache.ignite.ml.nn.architecture

Layer architecture.

LayerArchitecture(int) - Constructor for class org.apache.ignite.ml.nn.architecture.LayerArchitecture

Construct LayerArchitecture.

layerArchitecture(int) - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Get architecture of layer with given index.

layers - Variable in class org.apache.ignite.ml.nn.MultilayerPerceptron

List containing layers parameters.

layersCount() - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Count of layers in MLP.

layersCount() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get count of layers in this MLP.

LeafValuesComputer<T> - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Class containing logic of leaf values computing after building of all trees in random forest.

LeafValuesComputer() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

LearningEnvironment - Interface in org.apache.ignite.ml.environment

Specifies a set of utility-objects helpful at runtime but optional for learning algorithm (like thread pool for parallel learning in bagging model or logger).

learningEnvironment(Preprocessor<K, V>) - Method in interface org.apache.ignite.ml.preprocessing.PreprocessingTrainer

Returns local learning environment with initialized deploying context by base preptocessor.

learningEnvironment() - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Get learning environment.

LearningEnvironmentBuilder - Interface in org.apache.ignite.ml.environment

Builder of learning environment.

learnLabels(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Defines unique labels in dataset if need (useful in case of classification).

learnLabels(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBRegressionTrainer

Defines unique labels in dataset if need (useful in case of classification).

learnLabels(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Defines unique labels in dataset if need (useful in case of classification).

learnModels(DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Implementation of gradient boosting iterations.

length() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

LG - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns Math.log(a) / Math.log(b).

like(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

Creates new empty matrix of the same underlying class but of different size.

like(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Creates new empty matrix of the same underlying class but of different size.

like(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

Creates new empty matrix of the same underlying class but of different size.

like(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new empty matrix of the same underlying class but of different size.

like(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new empty vector of the same underlying class but of different cardinality.

like(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

Creates new empty vector of the same underlying class but of different cardinality.

like(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

Creates new empty vector of the same underlying class but of different cardinality.

like(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

Creates new empty vector of the same underlying class but of different cardinality.

like(int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

Creates new empty vector of the same underlying class but of different cardinality.

like(int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new empty vector of the same underlying class but of different cardinality.

like(Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the like matrix with read-only matrices support.

like(Matrix, int, int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the like matrix with specified size with read-only matrices support.

likeIdentity() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

likelihood(Vector) - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

likeMatrix(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new matrix of compatible flavor with given size.

likeMatrix(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DenseVector

Creates new matrix of compatible flavor with given size.

likeMatrix(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

Creates new matrix of compatible flavor with given size.

likeMatrix(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

Creates new matrix of compatible flavor with given size.

likeMatrix(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

Creates new matrix of compatible flavor with given size.

likeMatrix(int, int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new matrix of compatible flavor with given size.

likeVector(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.DenseMatrix

Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.

likeVector(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.

likeVector(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.

likeVector(int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new empty vector of compatible properties (similar or the same flavor) to this matrix.

likeVector(Matrix, int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the like vector with read-only matrices support.

likeVector(Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Create the like vector with read-only matrices support.

LINEAR - Static variable in class org.apache.ignite.ml.nn.Activators

Linear unit activation function.

linearOutput - Variable in class org.apache.ignite.ml.nn.MLPState

Output of linear transformations.

linearOutput(int) - Method in class org.apache.ignite.ml.nn.MLPState

Output of linear transformation of given layer.

LinearRegressionLSQRTrainer - Class in org.apache.ignite.ml.regressions.linear

Trainer of the linear regression model based on LSQR algorithm.

LinearRegressionLSQRTrainer() - Constructor for class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer

LinearRegressionModel - Class in org.apache.ignite.ml.regressions.linear

Simple linear regression model which predicts result value Y as a linear combination of input variables: Y = weights * X + intercept.

LinearRegressionModel(Vector, double) - Constructor for class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

LinearRegressionSGDTrainer<P extends Serializable> - Class in org.apache.ignite.ml.regressions.linear

Trainer of the linear regression model based on stochastic gradient descent algorithm.

LinearRegressionSGDTrainer(UpdatesStrategy<? super MultilayerPerceptron, P>, int, int, int, long) - Constructor for class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Constructs a new instance of linear regression SGD trainer.

LinearRegressionSGDTrainer(UpdatesStrategy<? super MultilayerPerceptron, P>) - Constructor for class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Constructs a new instance of linear regression SGD trainer.

listFiles(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Returns list of files in the specified directory.

listFiles(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Returns list of files in the specified directory.

load(P) - Method in interface org.apache.ignite.ml.Exporter

Load model representation object from p.

load(String) - Method in class org.apache.ignite.ml.FileExporter

Load model representation object from p.

loadDataset(MLSandboxDatasets) - Method in class org.apache.ignite.ml.util.SandboxMLCache

Loads dataset as a list of rows.

loadFromTxtFile(Path, String, boolean, boolean) - Static method in class org.apache.ignite.ml.structures.preprocessing.LabeledDatasetLoader

Datafile should keep class labels in the first column.

localCopyOf(Vector) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

TODO: IGNITE-5723, rewrite in a more optimal way.

LocalDataset<C extends Serializable,D extends AutoCloseable> - Class in org.apache.ignite.ml.dataset.impl.local

An implementation of dataset based on local data structures such as Map and List and doesn't require Ignite environment.

LocalDatasetBuilder<K,V> - Class in org.apache.ignite.ml.dataset.impl.local

A dataset builder that makes LocalDataset.

LocalDatasetBuilder(Map<K, V>, int) - Constructor for class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Constructs a new instance of local dataset builder that makes LocalDataset with default predicate that passes all upstream entries to dataset.

LocalDatasetBuilder(Map<K, V>, IgniteBiPredicate<K, V>, int, UpstreamTransformerBuilder) - Constructor for class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Constructs a new instance of local dataset builder that makes LocalDataset.

LocalDatasetBuilder(Map<K, V>, IgniteBiPredicate<K, V>, int) - Constructor for class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Constructs a new instance of local dataset builder that makes LocalDataset.

LocalLabelPairCursor<L,K,V,T> - Class in org.apache.ignite.ml.selection.scoring.cursor

Truth with prediction cursor based on a locally stored data.

LocalLabelPairCursor(Map<K, V>, IgniteBiPredicate<K, V>, Preprocessor<K, V>, IgniteModel<Vector, L>) - Constructor for class org.apache.ignite.ml.selection.scoring.cursor.LocalLabelPairCursor

Constructs a new instance of local truth with prediction cursor.

LocalModelDescriptorStorage - Class in org.apache.ignite.ml.inference.storage.descriptor

Model descriptor storage based on local hash map.

LocalModelDescriptorStorage() - Constructor for class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

LocalModelStorageProvider - Class in org.apache.ignite.ml.inference.storage.model

Implementation of ModelStorageProvider based on local ConcurrentHashMap.

LocalModelStorageProvider() - Constructor for class org.apache.ignite.ml.inference.storage.model.LocalModelStorageProvider

lock(String) - Method in class org.apache.ignite.ml.inference.storage.model.IgniteModelStorageProvider

Locks the specified path.

lock(String) - Method in class org.apache.ignite.ml.inference.storage.model.LocalModelStorageProvider

Locks the specified path.

lock(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorageProvider

Locks the specified path.

lockPaths(Supplier<T>, String...) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Locks paths in model storage and call passed method.

lockPaths(Supplier<T>, String...) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Locks paths in model storage and call passed method.

locStepUpdatesReducer() - Method in class org.apache.ignite.ml.nn.UpdatesStrategy

Get function used to reduce updates in one training (for example, sum all sequential gradient updates to get one gradient update).

log(Vector) - Method in class org.apache.ignite.ml.environment.logging.ConsoleLogger

Log vector.

log(MLLogger.VerboseLevel, IgniteModel<K, V>) - Method in class org.apache.ignite.ml.environment.logging.ConsoleLogger

Log model according to toString method.

log(MLLogger.VerboseLevel, String, Object...) - Method in class org.apache.ignite.ml.environment.logging.ConsoleLogger

Log line with formatting.

log(Vector) - Method in class org.apache.ignite.ml.environment.logging.CustomMLLogger

Log vector.

log(MLLogger.VerboseLevel, IgniteModel<K, V>) - Method in class org.apache.ignite.ml.environment.logging.CustomMLLogger

Log model according to toString method.

log(MLLogger.VerboseLevel, String, Object...) - Method in class org.apache.ignite.ml.environment.logging.CustomMLLogger

Log line with formatting.

log(Vector) - Method in interface org.apache.ignite.ml.environment.logging.MLLogger

Log vector.

log(MLLogger.VerboseLevel, IgniteModel<K, V>) - Method in interface org.apache.ignite.ml.environment.logging.MLLogger

Log model according to toString method.

log(MLLogger.VerboseLevel, String, Object...) - Method in interface org.apache.ignite.ml.environment.logging.MLLogger

Log line with formatting.

log(Vector) - Method in class org.apache.ignite.ml.environment.logging.NoOpLogger

Log vector.

log(MLLogger.VerboseLevel, IgniteModel<K, V>) - Method in class org.apache.ignite.ml.environment.logging.NoOpLogger

Log model according to toString method.

log(MLLogger.VerboseLevel, String, Object...) - Method in class org.apache.ignite.ml.environment.logging.NoOpLogger

Log line with formatting.

LOG - Static variable in class org.apache.ignite.ml.optimization.LossFunctions

Log loss function.

LOG2 - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns Math.log(a) / Math.log(2).

LOG2 - Static variable in class org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies

logger() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Returns an instance of logger.

logger(Class<T>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Returns an instance of logger for specific class.

LogisticRegressionModel - Class in org.apache.ignite.ml.regressions.logistic

Logistic regression (logit model) is a generalized linear model used for binomial regression.

LogisticRegressionModel(Vector, double) - Constructor for class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

LogisticRegressionSGDTrainer - Class in org.apache.ignite.ml.regressions.logistic

Trainer of the logistic regression model based on stochastic gradient descent algorithm.

LogisticRegressionSGDTrainer() - Constructor for class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

LogLoss - Class in org.apache.ignite.ml.composition.boosting.loss

Logistic regression loss function.

LogLoss() - Constructor for class org.apache.ignite.ml.composition.boosting.loss.LogLoss

logNormalize() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.

logNormalize(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.

logNormalize() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.

logNormalize(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.

logNormalize() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing the log(1 + entry) / L_2 norm values of this vector.

logNormalize(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector with a normalized value calculated as log_power(1 + entry) / L_power norm.

loss - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Loss of gradient.

loss - Variable in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Loss function.

Loss - Interface in org.apache.ignite.ml.composition.boosting.loss

Loss interface of computing error or gradient of error on specific row in dataset.

loss - Variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Loss function.

loss - Variable in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Loss function.

LossFunctions - Class in org.apache.ignite.ml.optimization

Class containing popular loss functions.

LossFunctions() - Constructor for class org.apache.ignite.ml.optimization.LossFunctions

LOW - Static variable in class org.apache.ignite.ml.environment.logging.ConsoleLogger.Factory

Low.

LRUCache<K,V> - Class in org.apache.ignite.ml.util

LRU cache with fixed size and expiration listener.

LRUCache(int) - Constructor for class org.apache.ignite.ml.util.LRUCache

Constructs a new instance of LRU cache.

LRUCache(int, LRUCacheExpirationListener<V>) - Constructor for class org.apache.ignite.ml.util.LRUCache

Constructs a new instance of LRU cache.

LRUCacheExpirationListener<V> - Interface in org.apache.ignite.ml.util

LRU cache expiration listener.

LSQROnHeap<K,V> - Class in org.apache.ignite.ml.math.isolve.lsqr

Distributed implementation of LSQR algorithm based on AbstractLSQR and Dataset.

LSQROnHeap(DatasetBuilder<K, V>, LearningEnvironmentBuilder, PartitionDataBuilder<K, V, LSQRPartitionContext, SimpleLabeledDatasetData>, LearningEnvironment) - Constructor for class org.apache.ignite.ml.math.isolve.lsqr.LSQROnHeap

Constructs a new instance of OnHeap LSQR algorithm implementation.

LSQRPartitionContext - Class in org.apache.ignite.ml.math.isolve.lsqr

Partition context of the LSQR algorithm.

LSQRPartitionContext() - Constructor for class org.apache.ignite.ml.math.isolve.lsqr.LSQRPartitionContext

LSQRResult - Class in org.apache.ignite.ml.math.isolve.lsqr

LSQR iterative solver result.

LSQRResult(double[], int, int, double, double, double, double, double, double, double[]) - Constructor for class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

Constructs a new instance of LSQR result.

LUDecomposition - Class in org.apache.ignite.ml.math.primitives.matrix

Calculates the LU-decomposition of a square matrix.

LUDecomposition(Matrix) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Calculates the LU-decomposition of the given matrix.

LUDecomposition(Matrix, double) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

Calculates the LUP-decomposition of the given matrix.

M

mae() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Returns mean absolute error.

makeBagged(DatasetTrainer<? extends IgniteModel, L>, int, double, PredictionsAggregator) - Static method in class org.apache.ignite.ml.trainers.TrainerTransformers

Add bagging logic to a given trainer.

makeBagged(DatasetTrainer<M, L>, int, double, int, int, PredictionsAggregator) - Static method in class org.apache.ignite.ml.trainers.TrainerTransformers

Add bagging logic to a given trainer.

makeElement(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

ManhattanDistance - Class in org.apache.ignite.ml.math.distances

Calculates the L₁ (sum of abs) distance between two points.

ManhattanDistance() - Constructor for class org.apache.ignite.ml.math.distances.ManhattanDistance

map(Object, Double) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer.Mapping

Add mapping.

map(List<ClusterNode>, List<Long>) - Method in class org.apache.ignite.ml.genetic.CrossOverTask

map(List<ClusterNode>, List<Long>) - Method in class org.apache.ignite.ml.genetic.FitnessTask

map(List<ClusterNode>, List<Long>) - Method in class org.apache.ignite.ml.genetic.MutateTask

map(List<ClusterNode>, LinkedHashMap<Long, Double>) - Method in class org.apache.ignite.ml.genetic.RouletteWheelSelectionTask

map(List<ClusterNode>, List<Long>) - Method in class org.apache.ignite.ml.genetic.TruncateSelectionTask

map(IgniteDoubleFunction<Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Maps all values in this matrix through a given function.

map(Matrix, IgniteBiFunction<Double, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Maps all values in this matrix through a given function.

map(IgniteDoubleFunction<Double>) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Maps all values in this matrix through a given function.

map(Matrix, IgniteBiFunction<Double, Double, Double>) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Maps all values in this matrix through a given function.

map(IgniteDoubleFunction<Double>) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Maps all values in this vector through a given function.

map(Vector, IgniteBiFunction<Double, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Maps all values in this vector through a given function.

map(IgniteBiFunction<Double, Double, Double>, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Maps all elements of this vector by applying given function to each element with a constant second parameter y.

map(IgniteDoubleFunction<Double>) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Maps all values in this vector through a given function.

map(Vector, IgniteBiFunction<Double, Double, Double>) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Maps all values in this vector through a given function.

map(IgniteBiFunction<Double, Double, Double>, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Maps all elements of this vector by applying given function to each element with a constant second parameter y.

map(IgniteDoubleFunction<Double>) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Maps all values in this vector through a given function.

map(Vector, IgniteBiFunction<Double, Double, Double>) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Maps all values in this vector through a given function.

map(IgniteBiFunction<Double, Double, Double>, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Maps all elements of this vector by applying given function to each element with a constant second parameter y.

map(IgniteFunction<LabeledVector<L1>, LabeledVector<L2>>) - Method in interface org.apache.ignite.ml.preprocessing.Preprocessor

Map vectorizer answer.

map(K, V) - Method in class org.apache.ignite.ml.selection.split.mapper.SHA256UniformMapper

Maps key-value pair to a point on the segment (0, 1).

map(K, V) - Method in interface org.apache.ignite.ml.selection.split.mapper.UniformMapper

Maps key-value pair to a point on the segment (0, 1).

map(IgniteFunction<Vector, Vector>) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Maps values of vector generator using mapper.

map(IgniteFunction<VectorGenerator, VectorGenerator>) - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.Builder

Adds map function for all generators in family.

mapFeatures(Vector) - Method in interface org.apache.ignite.ml.composition.DatasetMapping

Method used to map feature vectors.

mapLabels(L1) - Method in interface org.apache.ignite.ml.composition.DatasetMapping

Method used to map labels.

MappedPreprocessor<K,V,L0,L1> - Class in org.apache.ignite.ml.preprocessing.developer

Mapped Preprocessor.

MappedPreprocessor(Preprocessor<K, V>, IgniteFunction<LabeledVector<L0>, LabeledVector<L1>>) - Constructor for class org.apache.ignite.ml.preprocessing.developer.MappedPreprocessor

Creates an instance of MappedPreprocessor.

mapper - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Mapper.

Mapping() - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer.Mapping

mappingFeatures(IgniteFunction<Vector, Vector>) - Static method in interface org.apache.ignite.ml.composition.DatasetMapping

Dataset mapping which maps features, leaving labels unaffected.

mapToBucket(T) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Bucket mapping.

mapToBucket(BootstrappedVector) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.BootstrappedVectorsHistogram

Bucket mapping.

mapToCounter(T) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Counter mapping.

mapToCounter(BootstrappedVector) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.CountersHistogram

Counter mapping.

MapUtil - Class in org.apache.ignite.ml.math.util

Some Map related utils.

MapUtil() - Constructor for class org.apache.ignite.ml.math.util.MapUtil

mapVectors(IgniteFunction<Vector, Vector>) - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

Apply user defined mapper to vectors stream without labels hiding.

MathIllegalArgumentException - Exception in org.apache.ignite.ml.math.exceptions

Base class for all preconditions violation exceptions.

MathIllegalArgumentException(String, Object...) - Constructor for exception org.apache.ignite.ml.math.exceptions.MathIllegalArgumentException

MathRuntimeException - Exception in org.apache.ignite.ml.math.exceptions

This class is based on the corresponding class from Apache Common Math lib.

MathRuntimeException(String, Object...) - Constructor for exception org.apache.ignite.ml.math.exceptions.MathRuntimeException

MathRuntimeException(Throwable, String, Object...) - Constructor for exception org.apache.ignite.ml.math.exceptions.MathRuntimeException

Matrix - Interface in org.apache.ignite.ml.math.primitives.matrix

A matrix interface.

Matrix.Element - Interface in org.apache.ignite.ml.math.primitives.matrix

Holder for matrix's element.

MatrixFactorizationGradient<O extends Serializable,S extends Serializable> - Class in org.apache.ignite.ml.recommendation.util

Gradient of matrix factorization loss function.

MatrixFactorizationGradient(Map<O, Vector>, Map<S, Vector>, int) - Constructor for class org.apache.ignite.ml.recommendation.util.MatrixFactorizationGradient

Constructs a new instance of matrix factorization gradient.

MatrixStorage - Interface in org.apache.ignite.ml.math.primitives.matrix

Data storage support for Matrix.

MatrixUtil - Class in org.apache.ignite.ml.math.util

Utility class for various matrix operations.

MatrixUtil() - Constructor for class org.apache.ignite.ml.math.util.MatrixUtil

max() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

MAX_ABS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns max(abs(a), abs(b)).

MAX_GENERIC(T, T, Comparator<T>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Generic 'max' function.

MaxAbsScalerPartitionData - Class in org.apache.ignite.ml.preprocessing.maxabsscaling

Partition data used in maxabsscaling preprocessor.

MaxAbsScalerPartitionData(double[]) - Constructor for class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPartitionData

Constructs a new instance of maxabsscaling partition data.

MaxAbsScalerPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.maxabsscaling

The preprocessing function that makes maxabsscaling, transforms features to the scale [-1,+1].

MaxAbsScalerPreprocessor(double[], Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerPreprocessor

Constructs a new instance of maxabsscaling preprocessor.

MaxAbsScalerTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.maxabsscaling

Trainer of the maxabsscaling preprocessor.

MaxAbsScalerTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.maxabsscaling.MaxAbsScalerTrainer

maxElement() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the maximum element in this matrix.

maxElement() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the maximum element in this matrix.

maxElement() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets maximum element in this vector.

maxElement() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets maximum element in this vector.

maxElement() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets maximum element in this vector.

maxValue() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the maximum value in this matrix.

maxValue() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the maximum value in this matrix.

maxValue() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets maximum value in this vector.

maxValue() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets maximum value in this vector.

maxValue() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets maximum value in this vector.

mean() - Method in class org.apache.ignite.ml.clustering.gmm.NewComponentStatisticsAggregator

mean() - Method in class org.apache.ignite.ml.dataset.primitive.SimpleDataset

Calculates mean value by all columns.

mean() - Method in class org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution

mean() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

mean() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

MeanAbsValueConvergenceChecker<K,V> - Class in org.apache.ignite.ml.composition.boosting.convergence.mean

Use mean value of errors for estimating error on dataset.

MeanAbsValueConvergenceChecker(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>, double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceChecker

Creates an instance of MeanAbsValueConvergenceChecker.

MeanAbsValueConvergenceCheckerFactory - Class in org.apache.ignite.ml.composition.boosting.convergence.mean

Factory for MeanAbsValueConvergenceChecker.

MeanAbsValueConvergenceCheckerFactory(double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.mean.MeanAbsValueConvergenceCheckerFactory

MeanDecisionTreeLeafBuilder - Class in org.apache.ignite.ml.tree.leaf

Decision tree leaf node builder that chooses mean value as a leaf value.

MeanDecisionTreeLeafBuilder() - Constructor for class org.apache.ignite.ml.tree.leaf.MeanDecisionTreeLeafBuilder

meanLbVal - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Mean label value.

MeanValuePredictionsAggregator - Class in org.apache.ignite.ml.composition.predictionsaggregator

Predictions aggregator returning the mean value of predictions.

MeanValuePredictionsAggregator() - Constructor for class org.apache.ignite.ml.composition.predictionsaggregator.MeanValuePredictionsAggregator

MeanValueStatistic - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Statistics for mean value computing container.

MeanValueStatistic(double, long) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

Creates an instance of MeanValueStatistic.

MedianOfMedianConvergenceChecker<K,V> - Class in org.apache.ignite.ml.composition.boosting.convergence.median

Use median of median on partitions value of errors for estimating error on dataset.

MedianOfMedianConvergenceChecker(long, IgniteFunction<Double, Double>, Loss, DatasetBuilder<K, V>, Preprocessor<K, V>, double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceChecker

Creates an instance of MedianOfMedianConvergenceChecker.

MedianOfMedianConvergenceCheckerFactory - Class in org.apache.ignite.ml.composition.boosting.convergence.median

Factory for MedianOfMedianConvergenceChecker.

MedianOfMedianConvergenceCheckerFactory(double) - Constructor for class org.apache.ignite.ml.composition.boosting.convergence.median.MedianOfMedianConvergenceCheckerFactory

mergeLeafStats(ObjectHistogram<BootstrappedVector>, ObjectHistogram<BootstrappedVector>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.ClassifierLeafValuesComputer

Merge statistics for same leafs.

mergeLeafStats(T, T) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

Merge statistics for same leafs.

mergeLeafStats(MeanValueStatistic, MeanValueStatistic) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.RegressionLeafValuesComputer

Merge statistics for same leafs.

mergeMaps(M, M, BinaryOperator<V>, Supplier<M>) - Static method in class org.apache.ignite.ml.math.util.MapUtil

meta - Variable in class org.apache.ignite.ml.structures.Dataset

Metadata to identify feature.

meta() - Method in class org.apache.ignite.ml.structures.Dataset

MetaAttributes - Interface in org.apache.ignite.ml.math

Interface provides support for meta attributes on vectors and matrices.

metric - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Metric.

metric - Variable in class org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics

The main metric to get individual score.

Metric<L> - Interface in org.apache.ignite.ml.selection.scoring.metric

Base interface for score calculators.

MetricValues - Interface in org.apache.ignite.ml.selection.scoring.metric

Common interface to present metric values for different ML tasks.

MIN - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns min(a, b).

min() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

MIN_ABS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns min(abs(a), abs(b)).

MIN_GENERIC(T, T, Comparator<T>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Generic 'min' function.

minElement() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the minimum element in this matrix.

minElement() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the minimum element in this matrix.

minElement() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets minimal element in this vector.

minElement() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets minimal element in this vector.

minElement() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets minimal element in this vector.

MinMaxScalerPartitionData - Class in org.apache.ignite.ml.preprocessing.minmaxscaling

Partition data used in minmaxscaling preprocessor.

MinMaxScalerPartitionData(double[], double[]) - Constructor for class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPartitionData

Constructs a new instance of minmaxscaling partition data.

MinMaxScalerPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.minmaxscaling

Preprocessing function that makes minmaxscaling.

MinMaxScalerPreprocessor(double[], double[], Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerPreprocessor

Constructs a new instance of minmaxscaling preprocessor.

MinMaxScalerTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.minmaxscaling

Trainer of the minmaxscaling preprocessor.

MinMaxScalerTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.minmaxscaling.MinMaxScalerTrainer

MINUS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a - b.

minus(Matrix) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix where each value is a difference between corresponding value of this matrix and passed in argument matrix.

minus(Matrix) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix where each value is a difference between corresponding value of this matrix and passed in argument matrix.

minus(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing element by element difference between this vector and the argument one.

minus(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing element by element difference between this vector and the argument one.

minus(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing element by element difference between this vector and the argument one.

MINUS_ABS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns abs(a - b).

MINUS_SQUARED - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns (a - b) * (a - b)

minusMult(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns a - b*constant.

minValue() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets the minimum value in this matrix.

minValue() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets the minimum value in this matrix.

minValue() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets minimal value in this vector.

minValue() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets minimal value in this vector.

minValue() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets minimal value in this vector.

missRate() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Miss Rate or False Negative Rate (FNR).

mkdir(String, boolean) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Creates directory.

mkdir(String, boolean) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Creates directory.

mkdir(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Creates directory.

mkdirs(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Creates directory and all required parent directories in the path.

mkdirs(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Creates directory and all required parent directories in the path.

MLLogger - Interface in org.apache.ignite.ml.environment.logging

Helper for ML-specific objects logging.

MLLogger.Factory - Interface in org.apache.ignite.ml.environment.logging

MLLogger factory interface.

MLLogger.VerboseLevel - Enum in org.apache.ignite.ml.environment.logging

Logging verbose level.

MLPArchitecture - Class in org.apache.ignite.ml.nn.architecture

Class containing information about architecture of MLP.

MLPArchitecture(int) - Constructor for class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Construct an MLP architecture.

MLPInitializer - Interface in org.apache.ignite.ml.nn.initializers

Interface for classes encapsulating logic for initialization of weights and biases of MLP.

MLPLayer - Class in org.apache.ignite.ml.nn

Class containing information about layer.

MLPLayer(Matrix, Vector) - Constructor for class org.apache.ignite.ml.nn.MLPLayer

Construct MLPLayer from weights and biases.

MLPluginConfiguration - Class in org.apache.ignite.ml.util.plugin

Configuration of ML plugin that defines which ML inference services should be start up on Ignite startup.

MLPluginConfiguration() - Constructor for class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

MLPluginProvider - Class in org.apache.ignite.ml.util.plugin

Machine learning inference plugin provider.

MLPluginProvider() - Constructor for class org.apache.ignite.ml.util.plugin.MLPluginProvider

MLPState - Class in org.apache.ignite.ml.nn

State of MLP after computation.

MLPState(Matrix) - Constructor for class org.apache.ignite.ml.nn.MLPState

Construct MLP state.

MLPTrainer<P extends Serializable> - Class in org.apache.ignite.ml.nn

Multilayer perceptron trainer based on partition based Dataset.

MLPTrainer(MLPArchitecture, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>, UpdatesStrategy<? super MultilayerPerceptron, P>, int, int, int, long) - Constructor for class org.apache.ignite.ml.nn.MLPTrainer

Constructs a new instance of multilayer perceptron trainer.

MLPTrainer(IgniteFunction<Dataset<EmptyContext, SimpleLabeledDatasetData>, MLPArchitecture>, IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>, UpdatesStrategy<? super MultilayerPerceptron, P>, int, int, int, long) - Constructor for class org.apache.ignite.ml.nn.MLPTrainer

Constructs a new instance of multilayer perceptron trainer.

MLSandboxDatasets - Enum in org.apache.ignite.ml.util

The names of popular datasets used in examples.

mnistAsList(String, String, Random, int) - Static method in class org.apache.ignite.ml.util.MnistUtils

Read random count samples from MNIST dataset from two files (images and labels) into a stream of labeled vectors.

mnistAsListFromResource(String, String, Random, int) - Static method in class org.apache.ignite.ml.util.MnistUtils

Read random count samples from MNIST dataset from two resources (images and labels) into a stream of labeled vectors.

mnistAsStream(String, String, Random, int) - Static method in class org.apache.ignite.ml.util.MnistUtils

Read random count samples from MNIST dataset from two files (images and labels) into a stream of labeled vectors.

MnistImage(double[]) - Constructor for class org.apache.ignite.ml.util.MnistUtils.MnistImage

Construct a new instance of MNIST image.

MnistLabeledImage(double[], int) - Constructor for class org.apache.ignite.ml.util.MnistUtils.MnistLabeledImage

Constructs a new instance of MNIST labeled image.

MnistUtils - Class in org.apache.ignite.ml.util

Utility class for reading MNIST dataset.

MnistUtils() - Constructor for class org.apache.ignite.ml.util.MnistUtils

MnistUtils.MnistImage - Class in org.apache.ignite.ml.util

MNIST image.

MnistUtils.MnistLabeledImage - Class in org.apache.ignite.ml.util

MNIST labeled image.

MOD - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a % b.

Model<I,O> - Interface in org.apache.ignite.ml.inference

Inference model that can be used to make predictions.

MODEL_DESCRIPTOR_STORAGE_CACHE_NAME - Static variable in class org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorageFactory

Model descriptor storage cache name.

MODEL_STORAGE_CACHE_NAME - Static variable in class org.apache.ignite.ml.inference.storage.model.ModelStorageFactory

Model storage cache name.

ModelDescriptor - Class in org.apache.ignite.ml.inference

Model descriptor that encapsulates information about model, ModelReader and ModelParser which is required to build the model.

ModelDescriptor(String, String, ModelSignature, ModelReader, ModelParser<byte[], byte[], ?>) - Constructor for class org.apache.ignite.ml.inference.ModelDescriptor

Constructs a new instance of model descriptor.

ModelDescriptorStorage - Interface in org.apache.ignite.ml.inference.storage.descriptor

Storage that allows to load, keep and get access to model descriptors (see ModelDescriptor).

ModelDescriptorStorageFactory - Class in org.apache.ignite.ml.inference.storage.descriptor

Model descriptor storage factory.

ModelDescriptorStorageFactory() - Constructor for class org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorageFactory

ModelOnFeaturesSubspace - Class in org.apache.ignite.ml.composition

Model trained on a features subspace with mapping from original features space to subspace.

ModelParser<I,O,M extends Model<I,O>> - Interface in org.apache.ignite.ml.inference.parser

Model parser that accepts a serialized model represented by byte array, parses it and returns Model.

ModelReader - Interface in org.apache.ignite.ml.inference.reader

Model reader that reads model from external or internal storage and returns it in serialized form as byte array.

models() - Method in class org.apache.ignite.ml.composition.ModelsCompositionFormat

ModelsComposition - Class in org.apache.ignite.ml.composition

Model consisting of several models and prediction aggregation strategy.

ModelsComposition(List<? extends IgniteModel<Vector, Double>>, PredictionsAggregator) - Constructor for class org.apache.ignite.ml.composition.ModelsComposition

Constructs a new instance of composition of models.

ModelsCompositionFormat - Class in org.apache.ignite.ml.composition

ModelsComposition representation.

ModelsCompositionFormat(List<IgniteModel<Vector, Double>>, PredictionsAggregator) - Constructor for class org.apache.ignite.ml.composition.ModelsCompositionFormat

Creates an instance of ModelsCompositionFormat.

ModelSignature - Class in org.apache.ignite.ml.inference

Signature that defines input/output types in Protobuf.

ModelSignature(String, String, String) - Constructor for class org.apache.ignite.ml.inference.ModelSignature

Constructs a new instance of model signature.

ModelsParallelComposition<I,O> - Class in org.apache.ignite.ml.composition.combinators.parallel

Parallel composition of models.

ModelsParallelComposition(List<IgniteModel<I, O>>) - Constructor for class org.apache.ignite.ml.composition.combinators.parallel.ModelsParallelComposition

Construc an instance of this class from list of submodels.

ModelsSequentialComposition<I,O1,O2> - Class in org.apache.ignite.ml.composition.combinators.sequential

Sequential composition of models.

ModelsSequentialComposition(IgniteModel<I, O1>, IgniteModel<O1, O2>) - Constructor for class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Construct instance of this class from two given models.

ModelStorage - Interface in org.apache.ignite.ml.inference.storage.model

Storage that allows to load, keep and get access to model in byte representation.

ModelStorageFactory - Class in org.apache.ignite.ml.inference.storage.model

Model storage factory.

ModelStorageFactory() - Constructor for class org.apache.ignite.ml.inference.storage.model.ModelStorageFactory

ModelStorageModelReader - Class in org.apache.ignite.ml.inference.reader

Model reader that reads directory or file from model storage and serializes it using DirectorySerializer.

ModelStorageModelReader(String, IgniteSupplier<ModelStorage>) - Constructor for class org.apache.ignite.ml.inference.reader.ModelStorageModelReader

Constructs a new instance of model storage inference model builder.

ModelStorageModelReader(String) - Constructor for class org.apache.ignite.ml.inference.reader.ModelStorageModelReader

Constructs a new instance of model storage inference model builder.

ModelStorageProvider - Interface in org.apache.ignite.ml.inference.storage.model

Model storage provider that keeps files and directories presented as FileOrDirectory files and correspondent locks.

ModelStorateThinClientProcessor - Class in org.apache.ignite.ml.inference.storage.model.thinclient

Processor for model storage commands in thin client.

ModelStorateThinClientProcessor(ModelStorage) - Constructor for class org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor

Creates an instance of model storage commands processor.

ModelStorateThinClientProcessor.Method - Enum in org.apache.ignite.ml.inference.storage.model.thinclient

Operations of model storage for GGFS client.

ModelTrace - Class in org.apache.ignite.ml.util

Helper for model tracing.

momentum - Variable in class org.apache.ignite.ml.optimization.updatecalculators.NesterovUpdateCalculator

Momentum constant.

MostCommonDecisionTreeLeafBuilder - Class in org.apache.ignite.ml.tree.leaf

Decision tree leaf node builder that chooses most common value as a leaf node value.

MostCommonDecisionTreeLeafBuilder() - Constructor for class org.apache.ignite.ml.tree.leaf.MostCommonDecisionTreeLeafBuilder

move(Vector) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Moves all vectors to other position by summing with input vector.

MSE - Static variable in class org.apache.ignite.ml.optimization.LossFunctions

Mean squared error loss function.

mse() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Returns mean squared error.

MSEHistogram - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Class contains implementation of splitting point finding algorithm based on MSE metric (see https://en.wikipedia.org/wiki/Mean_squared_error) and represents a set of histograms in according to this metric.

MSEHistogram(int, BucketMeta) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

Creates an instance of MSEHistogram.

MSEHistogramComputer - Class in org.apache.ignite.ml.tree.randomforest.data.impurity

Histogram computer realization for MSE impurity metric.

MSEHistogramComputer() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogramComputer

MSEImpurityMeasure - Class in org.apache.ignite.ml.tree.impurity.mse

Mean squared error (variance) impurity measure which is calculated the following way: \frac{1}{L}\sum_{i=0}^{n}(y_i - \mu)^2.

MSEImpurityMeasure(double, double, long, double, double, long) - Constructor for class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

Constructs a new instance of mean squared error (variance) impurity measure.

MSEImpurityMeasureCalculator - Class in org.apache.ignite.ml.tree.impurity.mse

Meas squared error (variance) impurity measure calculator.

MSEImpurityMeasureCalculator(boolean) - Constructor for class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasureCalculator

Constructs an instance of MSEImpurityMeasureCalculator.

MULT - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a * b.

mult(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns a * b.

MultiClassModel<M extends IgniteModel<Vector,Double>> - Class in org.apache.ignite.ml.multiclass

Base class for multi-classification model for set of classifiers.

MultiClassModel() - Constructor for class org.apache.ignite.ml.multiclass.MultiClassModel

MultiLabelDatasetTrainer<M extends IgniteModel> - Class in org.apache.ignite.ml.trainers

Interface for trainers that trains on dataset with multiple label per object.

MultiLabelDatasetTrainer() - Constructor for class org.apache.ignite.ml.trainers.MultiLabelDatasetTrainer

MultilayerPerceptron - Class in org.apache.ignite.ml.nn

Class encapsulating logic of multilayer perceptron.

MultilayerPerceptron(MLPArchitecture, MLPInitializer) - Constructor for class org.apache.ignite.ml.nn.MultilayerPerceptron

Construct MLP from given architecture and parameters initializer.

MultilayerPerceptron(MLPArchitecture) - Constructor for class org.apache.ignite.ml.nn.MultilayerPerceptron

Construct MLP from given architecture.

MultilayerPerceptron(MultilayerPerceptron, MultilayerPerceptron) - Constructor for class org.apache.ignite.ml.nn.MultilayerPerceptron

Create MLP from two MLPs: first stacked on second.

MultivariateGaussianDistribution - Class in org.apache.ignite.ml.math.stat

Distribution represents multidimentional gaussian distribution.

MultivariateGaussianDistribution(Vector, Matrix) - Constructor for class org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution

Constructs an instance of MultivariateGaussianDistribution.

MutateJob - Class in org.apache.ignite.ml.genetic

Responsible for applying mutation on respective Chromosome based on mutation Rate

MutateJob(Long, List<Long>, double) - Constructor for class org.apache.ignite.ml.genetic.MutateJob

MutateTask - Class in org.apache.ignite.ml.genetic

Responsible for applying mutation on respective chromosomes.

MutateTask(GAConfiguration) - Constructor for class org.apache.ignite.ml.genetic.MutateTask

N

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Fmeasure

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Precision

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Recall

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Returns the metric's name.

name() - Method in interface org.apache.ignite.ml.selection.scoring.metric.Metric

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetrics

Returns the metric's name.

name() - Method in class org.apache.ignite.ml.structures.FeatureMetadata

name() - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

NamedVector - Interface in org.apache.ignite.ml.math.primitives.vector

A named vector interface based on Vector.

NEGATE - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns -a.

negativeClsLb() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

negativeClsLb() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Get the negative label.

NesterovParameterUpdate - Class in org.apache.ignite.ml.optimization.updatecalculators

Data needed for Nesterov parameters updater.

NesterovParameterUpdate(int) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Construct NesterovParameterUpdate.

NesterovParameterUpdate(Vector) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Construct NesterovParameterUpdate.

NesterovUpdateCalculator<M extends SmoothParametrized<M>> - Class in org.apache.ignite.ml.optimization.updatecalculators

Class encapsulating Nesterov algorithm for MLP parameters updateCache.

NesterovUpdateCalculator(double, double) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.NesterovUpdateCalculator

Construct NesterovUpdateCalculator.

neuronsCount() - Method in class org.apache.ignite.ml.nn.architecture.LayerArchitecture

Get count of neurons in layer.

NewComponentStatisticsAggregator - Class in org.apache.ignite.ml.clustering.gmm

Class for aggregate statistics for finding new mean for GMM.

NewComponentStatisticsAggregator(long, long, Vector) - Constructor for class org.apache.ignite.ml.clustering.gmm.NewComponentStatisticsAggregator

Creates an instance of NewComponentStatisticsAggregator.

NewComponentStatisticsAggregator() - Constructor for class org.apache.ignite.ml.clustering.gmm.NewComponentStatisticsAggregator

Creates an instance of NewComponentStatisticsAggregator.

newInstance() - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

Creates an instance of ObjectHistogram from child class.

newInstance() - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.basic.CountersHistogram

Creates an instance of ObjectHistogram from child class.

next() - Method in class org.apache.ignite.ml.dataset.impl.cache.util.IteratorWithConcurrentModificationChecker

NNClassificationModel - Class in org.apache.ignite.ml.knn

Common methods and fields for all kNN and aNN models to predict label based on neighbours' labels.

NNClassificationModel() - Constructor for class org.apache.ignite.ml.knn.NNClassificationModel

NO_PARALLELISM - Static variable in interface org.apache.ignite.ml.environment.parallelism.ParallelismStrategy

No parallelism.

NoDataException - Exception in org.apache.ignite.ml.math.exceptions

This class is based on the corresponding class from Apache Common Math lib.

NoDataException() - Constructor for exception org.apache.ignite.ml.math.exceptions.NoDataException

Construct the exception.

NoDataException(String) - Constructor for exception org.apache.ignite.ml.math.exceptions.NoDataException

Construct the exception with a specific message.

NodeId - Class in org.apache.ignite.ml.tree.randomforest.data

Class represents Node id in Random Forest consisting of tree id and node id in tree in according to breadth-first search in tree.

NodeId(int, long) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.NodeId

Create an instance of NodeId.

nodeId() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeId

NodeImpurityHistograms(NodeId) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer.NodeImpurityHistograms

Create an instance of NodeImpurityHistograms.

NodeSplit - Class in org.apache.ignite.ml.tree.randomforest.data

Class represents a split point for decision tree.

NodeSplit(int, double, double, double) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

Creates an instance of NodeSplit.

noisify(RandomProducer) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Adds noize to all components of generated vectors.

noizify(IgniteFunction<Double, Double>) - Method in interface org.apache.ignite.ml.util.generators.primitives.scalar.RandomProducer

Adds value generated by random producer to function value.

noizify(Vector) - Method in interface org.apache.ignite.ml.util.generators.primitives.scalar.RandomProducer

Adds values generated by random producer to each vector value.

NoLabelVectorException - Exception in org.apache.ignite.ml.math.exceptions.knn

Shows Labeled Dataset index with non-existing Labeled Vector.

NoLabelVectorException(int) - Constructor for exception org.apache.ignite.ml.math.exceptions.knn.NoLabelVectorException

Creates new exception.

NonSquareMatrixException - Exception in org.apache.ignite.ml.math.exceptions

Indicates that given matrix is not a square matrix.

NonSquareMatrixException(int, int) - Constructor for exception org.apache.ignite.ml.math.exceptions.NonSquareMatrixException

Creates new square size violation exception.

nonZeroElements() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets number of non-zero elements in this matrix.

nonZeroElements() - Method in class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Gets number of non-zero elements in this matrix.

nonZeroElements() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets number of non-zero elements in this matrix.

nonZeroElements() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets number of non-zero elements in this vector.

nonZeroElements() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets number of non-zero elements in this vector.

nonZeroElements() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets number of non-zero elements in this vector.

nonZeroes() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Iterates ove all non-zero elements in this vector.

nonZeroes() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Iterates ove all non-zero elements in this vector.

nonZeroes() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Iterates ove all non-zero elements in this vector.

nonZeroSpliterator() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets spliterator for all non-zero values in this matrix.

nonZeroSpliterator() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets spliterator for all non-zero values in this matrix.

nonZeroSpliterator() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets spliterator for all non-zero values in this vector.

nonZeroSpliterator() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets spliterator for all non-zero values in this vector.

nonZeroSpliterator() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

Gets spliterator for all non-zero values in this vector.

nonZeroSpliterator() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets spliterator for all non-zero values in this vector.

NoOpLogger - Class in org.apache.ignite.ml.environment.logging

MLLogger implementation skipping all logs.

NoOpLogger() - Constructor for class org.apache.ignite.ml.environment.logging.NoOpLogger

NoParallelismStrategy - Class in org.apache.ignite.ml.environment.parallelism

All tasks should be processed in one thread.

NoParallelismStrategy.Stub<T> - Class in org.apache.ignite.ml.environment.parallelism

Stub for Future interface implementation.

NormalDistributionStatistics - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Aggregator of normal distribution statistics for continual features.

NormalDistributionStatistics(double, double, double, double, long) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

Creates an instance of NormalDistributionStatistics.

NormalDistributionStatisticsComputer - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Normal distribution parameters computer logic.

NormalDistributionStatisticsComputer() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatisticsComputer

NormalizationPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.normalization

Preprocessing function that makes normalization.

NormalizationPreprocessor(int, Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor

Constructs a new instance of Normalization preprocessor.

NormalizationTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.normalization

Trainer of the Normalization preprocessor.

NormalizationTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer

normalize() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing the normalized (L_2 norm) values of this vector.

normalize(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing the normalized (L_power norm) values of this vector.

normalize() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing the normalized (L_2 norm) values of this vector.

normalize(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing the normalized (L_power norm) values of this vector.

normalize() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing the normalized (L_2 norm) values of this vector.

normalize(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing the normalized (L_power norm) values of this vector.

npv() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Negative Predictive Value (NPV).

num2Arr(double) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Turn number to 1-sized array.

num2Vec(double) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Wrap specified value into vector.

O

ObjectHistogram<T> - Class in org.apache.ignite.ml.dataset.feature

Basic implementation of Histogram that implements also DistributionComputer.

ObjectHistogram() - Constructor for class org.apache.ignite.ml.dataset.feature.ObjectHistogram

ObjectSubjectPair<O extends Serializable,S extends Serializable> - Class in org.apache.ignite.ml.recommendation

Object-subject pair.

ObjectSubjectPair(O, S) - Constructor for class org.apache.ignite.ml.recommendation.ObjectSubjectPair

Constructs a new instance of object-subject pair.

ObjectSubjectRatingTriplet<O extends Serializable,S extends Serializable> - Class in org.apache.ignite.ml.recommendation

Object-subject-rating triplet.

ObjectSubjectRatingTriplet(O, S, Double) - Constructor for class org.apache.ignite.ml.recommendation.ObjectSubjectRatingTriplet

Constructs a new instance of object-subject-rating triplet.

of(List<T>) - Static method in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

Create parallel composition of trainers contained in a given list.

of(double...) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Creates dense local on heap vector based on array of doubles.

of(Double[]) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Creates vector based on array of Doubles.

of(Map<String, Double>) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Creates named vector based on map of keys and values.

of(DatasetTrainer<M, L>) - Static method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Construct instance of this class from a given DatasetTrainer.

OFF - Static variable in class org.apache.ignite.ml.environment.logging.ConsoleLogger.Factory

Offset.

offset() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

ofSame(List<? extends IgniteModel<I, O>>, IgniteFunction<O, I>) - Static method in class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Get sequential composition of submodels with same type.

ofSame(DatasetTrainer<? extends IgniteModel<I, O>, L>, IgniteBiFunction<Integer, ? super IgniteModel<I, O>, IgniteFunction<LabeledVector<L>, LabeledVector<L>>>, IgniteBiPredicate<Integer, IgniteModel<I, O>>, IgniteFunction<O, I>) - Static method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Construct sequential composition of same trainers.

ON_DEFAULT_POOL - Static variable in interface org.apache.ignite.ml.environment.parallelism.ParallelismStrategy

On default pool.

ONE_THIRD - Static variable in class org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies

oneHot(int, int) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Turn number into a local Vector of given size with one-hot encoding.

oneHot(int, int, boolean) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Turn number into Vector of given size with one-hot encoding.

OneHotEncoderPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.encoding.onehotencoder

Preprocessing function that makes one-hot encoding.

OneHotEncoderPreprocessor(Map<String, Integer>[], Preprocessor<K, V>, Set<Integer>) - Constructor for class org.apache.ignite.ml.preprocessing.encoding.onehotencoder.OneHotEncoderPreprocessor

Constructs a new instance of One Hot Encoder preprocessor.

OneVsRestTrainer<M extends IgniteModel<Vector,Double>> - Class in org.apache.ignite.ml.multiclass

This is a common heuristic trainer for multi-class labeled models.

OneVsRestTrainer(SingleLabelDatasetTrainer<M>) - Constructor for class org.apache.ignite.ml.multiclass.OneVsRestTrainer

onIgniteStart() - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

onIgniteStop(boolean) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

OnMajorityPredictionsAggregator - Class in org.apache.ignite.ml.composition.predictionsaggregator

Predictions aggregator returning the most frequently prediction.

OnMajorityPredictionsAggregator() - Constructor for class org.apache.ignite.ml.composition.predictionsaggregator.OnMajorityPredictionsAggregator

OrderedMatrix - Interface in org.apache.ignite.ml.math.primitives.matrix

Interface for matrix with particular order for storing entities.

org.apache.ignite.ml - package org.apache.ignite.ml

Root ML package.

org.apache.ignite.ml.clustering - package org.apache.ignite.ml.clustering

Contains clustering algorithms.

org.apache.ignite.ml.clustering.gmm - package org.apache.ignite.ml.clustering.gmm

Contains Gauss Mixture Model clustering algorithm (see GmmModel).

org.apache.ignite.ml.clustering.kmeans - package org.apache.ignite.ml.clustering.kmeans

Contains kMeans clustering algorithm.

org.apache.ignite.ml.composition - package org.apache.ignite.ml.composition

Contains classes for ensemble of models implementation.

org.apache.ignite.ml.composition.bagging - package org.apache.ignite.ml.composition.bagging

Contains bootstrap aggregation (bagging) trainer allowing to combine some other trainers and return a bagged version of them.

org.apache.ignite.ml.composition.boosting - package org.apache.ignite.ml.composition.boosting

Contains Gradient Boosting regression and classification abstract classes allowing regressor type selecting in child classes.

org.apache.ignite.ml.composition.boosting.convergence - package org.apache.ignite.ml.composition.boosting.convergence

Package contains implementation of convergency checking algorithms for gradient boosting.

org.apache.ignite.ml.composition.boosting.convergence.mean - package org.apache.ignite.ml.composition.boosting.convergence.mean

Contains implementation of convergence checking computer by mean of absolute value of errors in dataset.

org.apache.ignite.ml.composition.boosting.convergence.median - package org.apache.ignite.ml.composition.boosting.convergence.median

Contains implementation of convergence checking computer by median of medians of errors in dataset.

org.apache.ignite.ml.composition.boosting.convergence.simple - package org.apache.ignite.ml.composition.boosting.convergence.simple

Contains implementation of Stub for convergence checking.

org.apache.ignite.ml.composition.boosting.loss - package org.apache.ignite.ml.composition.boosting.loss

Contains loss functions for Gradient Boosting algorithms.

org.apache.ignite.ml.composition.combinators - package org.apache.ignite.ml.composition.combinators

Contains various combinators of trainers and models.

org.apache.ignite.ml.composition.combinators.parallel - package org.apache.ignite.ml.composition.combinators.parallel

Contains parallel combinators of trainers and models.

org.apache.ignite.ml.composition.combinators.sequential - package org.apache.ignite.ml.composition.combinators.sequential

Contains sequential combinators of trainers and models.

org.apache.ignite.ml.composition.predictionsaggregator - package org.apache.ignite.ml.composition.predictionsaggregator

Contains classes for several predictions aggregation strategies working with predictions vector from models ensemble.

org.apache.ignite.ml.composition.stacking - package org.apache.ignite.ml.composition.stacking

Contains classes used for training with stacking technique.

org.apache.ignite.ml.dataset - package org.apache.ignite.ml.dataset

Base package for machine learning dataset classes.

org.apache.ignite.ml.dataset.feature - package org.apache.ignite.ml.dataset.feature

Package for helper classes over features such as ObjectHistogram or FeatureMeta.

org.apache.ignite.ml.dataset.feature.extractor - package org.apache.ignite.ml.dataset.feature.extractor

Package for upstream object vectorizations.

org.apache.ignite.ml.dataset.feature.extractor.impl - package org.apache.ignite.ml.dataset.feature.extractor.impl

Package contains default implementations of Vectorizer.

org.apache.ignite.ml.dataset.impl - package org.apache.ignite.ml.dataset.impl

Base package for implementations of machine learning dataset.

org.apache.ignite.ml.dataset.impl.bootstrapping - package org.apache.ignite.ml.dataset.impl.bootstrapping

Base package for bootstrapped implementation of machine learning dataset.

org.apache.ignite.ml.dataset.impl.cache - package org.apache.ignite.ml.dataset.impl.cache

Base package for cache based implementation of machine learning dataset.

org.apache.ignite.ml.dataset.impl.cache.util - package org.apache.ignite.ml.dataset.impl.cache.util

Contains util classes used in cache based implementation of dataset.

org.apache.ignite.ml.dataset.impl.local - package org.apache.ignite.ml.dataset.impl.local

Base package for local implementation of machine learning dataset.

org.apache.ignite.ml.dataset.primitive - package org.apache.ignite.ml.dataset.primitive

Package that contains basic primitives build on top of Dataset.

org.apache.ignite.ml.dataset.primitive.builder - package org.apache.ignite.ml.dataset.primitive.builder

Base package for partition data and context builders.

org.apache.ignite.ml.dataset.primitive.builder.context - package org.apache.ignite.ml.dataset.primitive.builder.context

Contains partition context builders.

org.apache.ignite.ml.dataset.primitive.builder.data - package org.apache.ignite.ml.dataset.primitive.builder.data

Contains partition data builders.

org.apache.ignite.ml.dataset.primitive.context - package org.apache.ignite.ml.dataset.primitive.context

Contains implementation of partition context.

org.apache.ignite.ml.dataset.primitive.data - package org.apache.ignite.ml.dataset.primitive.data

Contains implementation of partition data.

org.apache.ignite.ml.environment - package org.apache.ignite.ml.environment

Package contains environment utils for ML algorithms.

org.apache.ignite.ml.environment.deploy - package org.apache.ignite.ml.environment.deploy

Package contains user-defined classes deploy support tools.

org.apache.ignite.ml.environment.logging - package org.apache.ignite.ml.environment.logging

Package contains several logging strategy realisations.

org.apache.ignite.ml.environment.parallelism - package org.apache.ignite.ml.environment.parallelism

Package contains realisations of parallelism strategies for multi-thread algorithms.

org.apache.ignite.ml.genetic - package org.apache.ignite.ml.genetic

Root GA package (GA Grid)

org.apache.ignite.ml.genetic.cache - package org.apache.ignite.ml.genetic.cache

Contains cache configurations for GA Grid

org.apache.ignite.ml.genetic.functions - package org.apache.ignite.ml.genetic.functions

Contains functions used for GA Grid

org.apache.ignite.ml.genetic.parameter - package org.apache.ignite.ml.genetic.parameter

Contains parameters used for GA Grid

org.apache.ignite.ml.genetic.utils - package org.apache.ignite.ml.genetic.utils

Contains utils for GA Grid

org.apache.ignite.ml.inference - package org.apache.ignite.ml.inference

Root package for model inference functionality.

org.apache.ignite.ml.inference.builder - package org.apache.ignite.ml.inference.builder

Root package for model inference builders.

org.apache.ignite.ml.inference.parser - package org.apache.ignite.ml.inference.parser

Root package for model inference parsers.

org.apache.ignite.ml.inference.reader - package org.apache.ignite.ml.inference.reader

Root package for model inference readers.

org.apache.ignite.ml.inference.storage - package org.apache.ignite.ml.inference.storage

Root package for inference model storages.

org.apache.ignite.ml.inference.storage.descriptor - package org.apache.ignite.ml.inference.storage.descriptor

Root package for inference model descriptor storages.

org.apache.ignite.ml.inference.storage.model - package org.apache.ignite.ml.inference.storage.model

Root package for inference model storages.

org.apache.ignite.ml.inference.storage.model.thinclient - package org.apache.ignite.ml.inference.storage.model.thinclient

Package contains classes for thin client operations with model storage.

org.apache.ignite.ml.inference.util - package org.apache.ignite.ml.inference.util

Root package for util classes used in org.apache.ignite.ml.inference package.

org.apache.ignite.ml.knn - package org.apache.ignite.ml.knn

Contains main APIs for kNN algorithms.

org.apache.ignite.ml.knn.ann - package org.apache.ignite.ml.knn.ann

Contains main APIs for ANN classification algorithms.

org.apache.ignite.ml.knn.classification - package org.apache.ignite.ml.knn.classification

Contains main APIs for kNN classification algorithms.

org.apache.ignite.ml.knn.regression - package org.apache.ignite.ml.knn.regression

Contains helper classes for kNN regression algorithms.

org.apache.ignite.ml.knn.utils - package org.apache.ignite.ml.knn.utils

Contains util functionality for kNN algorithms.

org.apache.ignite.ml.knn.utils.indices - package org.apache.ignite.ml.knn.utils.indices

Contains utils functionality for indices in kNN algorithms.

org.apache.ignite.ml.math - package org.apache.ignite.ml.math

Contains main APIs for matrix/vector algebra.

org.apache.ignite.ml.math.distances - package org.apache.ignite.ml.math.distances

Contains main APIs for distances.

org.apache.ignite.ml.math.exceptions - package org.apache.ignite.ml.math.exceptions

Contains exceptions for distributed code algebra.

org.apache.ignite.ml.math.exceptions.knn - package org.apache.ignite.ml.math.exceptions.knn

Contains exceptions for kNN algorithms.

org.apache.ignite.ml.math.exceptions.preprocessing - package org.apache.ignite.ml.math.exceptions.preprocessing

Contains exceptions for preprocessing.

org.apache.ignite.ml.math.functions - package org.apache.ignite.ml.math.functions

Contains serializable functions for distributed code algebra.

org.apache.ignite.ml.math.isolve - package org.apache.ignite.ml.math.isolve

Contains iterative algorithms for solving linear systems.

org.apache.ignite.ml.math.isolve.lsqr - package org.apache.ignite.ml.math.isolve.lsqr

Contains LSQR algorithm implementation.

org.apache.ignite.ml.math.primitives - package org.apache.ignite.ml.math.primitives

Contains classes for vector/matrix algebra.

org.apache.ignite.ml.math.primitives.matrix - package org.apache.ignite.ml.math.primitives.matrix

Contains matrix related classes.

org.apache.ignite.ml.math.primitives.matrix.impl - package org.apache.ignite.ml.math.primitives.matrix.impl

Contains several matrix implementations.

org.apache.ignite.ml.math.primitives.matrix.storage - package org.apache.ignite.ml.math.primitives.matrix.storage

Contains several matrix storages.

org.apache.ignite.ml.math.primitives.vector - package org.apache.ignite.ml.math.primitives.vector

Contains vector related classes.

org.apache.ignite.ml.math.primitives.vector.impl - package org.apache.ignite.ml.math.primitives.vector.impl

Contains several vector implementations.

org.apache.ignite.ml.math.primitives.vector.storage - package org.apache.ignite.ml.math.primitives.vector.storage

Contains several vector storages.

org.apache.ignite.ml.math.stat - package org.apache.ignite.ml.math.stat

Contains utility classes for distributions.

org.apache.ignite.ml.math.util - package org.apache.ignite.ml.math.util

Some math utils.

org.apache.ignite.ml.multiclass - package org.apache.ignite.ml.multiclass

Contains various multi-classifier models and trainers.

org.apache.ignite.ml.naivebayes - package org.apache.ignite.ml.naivebayes

Contains various naive Bayes classifiers.

org.apache.ignite.ml.naivebayes.discrete - package org.apache.ignite.ml.naivebayes.discrete

Contains Bernoulli naive Bayes classifier.

org.apache.ignite.ml.naivebayes.gaussian - package org.apache.ignite.ml.naivebayes.gaussian

Contains Gaussian naive Bayes classifier.

org.apache.ignite.ml.nn - package org.apache.ignite.ml.nn

Contains neural networks and related classes.

org.apache.ignite.ml.nn.architecture - package org.apache.ignite.ml.nn.architecture

Contains multilayer perceptron architecture classes.

org.apache.ignite.ml.nn.initializers - package org.apache.ignite.ml.nn.initializers

Contains multilayer perceptron parameters initializers.

org.apache.ignite.ml.optimization - package org.apache.ignite.ml.optimization

Contains implementations of optimization algorithms and related classes.

org.apache.ignite.ml.optimization.updatecalculators - package org.apache.ignite.ml.optimization.updatecalculators

Contains update calculators.

org.apache.ignite.ml.pipeline - package org.apache.ignite.ml.pipeline

Contains Pipeline API.

org.apache.ignite.ml.preprocessing - package org.apache.ignite.ml.preprocessing

Base package for machine learning preprocessing classes.

org.apache.ignite.ml.preprocessing.binarization - package org.apache.ignite.ml.preprocessing.binarization

Contains binarization preprocessor.

org.apache.ignite.ml.preprocessing.developer - package org.apache.ignite.ml.preprocessing.developer

Contains Developer API preprocessors.

org.apache.ignite.ml.preprocessing.encoding - package org.apache.ignite.ml.preprocessing.encoding

Contains encoding preprocessors.

org.apache.ignite.ml.preprocessing.encoding.onehotencoder - package org.apache.ignite.ml.preprocessing.encoding.onehotencoder

Contains one hot encoding preprocessor.

org.apache.ignite.ml.preprocessing.encoding.stringencoder - package org.apache.ignite.ml.preprocessing.encoding.stringencoder

Contains string encoding preprocessor.

org.apache.ignite.ml.preprocessing.imputing - package org.apache.ignite.ml.preprocessing.imputing

Contains Imputer preprocessor.

org.apache.ignite.ml.preprocessing.maxabsscaling - package org.apache.ignite.ml.preprocessing.maxabsscaling

Contains Max Abs Scaler preprocessor.

org.apache.ignite.ml.preprocessing.minmaxscaling - package org.apache.ignite.ml.preprocessing.minmaxscaling

Contains Min Max Scaler preprocessor.

org.apache.ignite.ml.preprocessing.normalization - package org.apache.ignite.ml.preprocessing.normalization

Contains Normalizer preprocessor.

org.apache.ignite.ml.preprocessing.standardscaling - package org.apache.ignite.ml.preprocessing.standardscaling

Contains Standard scaler preprocessor.

org.apache.ignite.ml.recommendation - package org.apache.ignite.ml.recommendation

Contains recommendation system framework.

org.apache.ignite.ml.recommendation.util - package org.apache.ignite.ml.recommendation.util

Contains util classes used in recommendation system framework.

org.apache.ignite.ml.regressions - package org.apache.ignite.ml.regressions

Contains various regressions.

org.apache.ignite.ml.regressions.linear - package org.apache.ignite.ml.regressions.linear

Contains various linear regressions.

org.apache.ignite.ml.regressions.logistic - package org.apache.ignite.ml.regressions.logistic

Contains various logistic regressions.

org.apache.ignite.ml.selection - package org.apache.ignite.ml.selection

Root package for dataset splitters, cross validation and search through parameters.

org.apache.ignite.ml.selection.cv - package org.apache.ignite.ml.selection.cv

Root package for cross-validation algorithms.

org.apache.ignite.ml.selection.paramgrid - package org.apache.ignite.ml.selection.paramgrid

Root package for parameter grid.

org.apache.ignite.ml.selection.scoring - package org.apache.ignite.ml.selection.scoring

Root package for score calculators.

org.apache.ignite.ml.selection.scoring.cursor - package org.apache.ignite.ml.selection.scoring.cursor

Util classes used for score calculation.

org.apache.ignite.ml.selection.scoring.evaluator - package org.apache.ignite.ml.selection.scoring.evaluator

Package for model evaluator classes.

org.apache.ignite.ml.selection.scoring.metric - package org.apache.ignite.ml.selection.scoring.metric

Root package for metrics.

org.apache.ignite.ml.selection.scoring.metric.classification - package org.apache.ignite.ml.selection.scoring.metric.classification

Root package for classification metrics.

org.apache.ignite.ml.selection.scoring.metric.exceptions - package org.apache.ignite.ml.selection.scoring.metric.exceptions

Root package for exceptions.

org.apache.ignite.ml.selection.scoring.metric.regression - package org.apache.ignite.ml.selection.scoring.metric.regression

Root package for regression metrics.

org.apache.ignite.ml.selection.split - package org.apache.ignite.ml.selection.split

Root package for dataset splitters and cross validation.

org.apache.ignite.ml.selection.split.mapper - package org.apache.ignite.ml.selection.split.mapper

Root package for mappers used in dataset splitters.

org.apache.ignite.ml.sql - package org.apache.ignite.ml.sql

Contains util classes that help to work with machine learning models in SQL and train models on data from SQL tables.

org.apache.ignite.ml.structures - package org.apache.ignite.ml.structures

Contains some internal utility structures.

org.apache.ignite.ml.structures.partition - package org.apache.ignite.ml.structures.partition

Contains internal APIs for dataset partitioned labeled datasets.

org.apache.ignite.ml.structures.preprocessing - package org.apache.ignite.ml.structures.preprocessing

Contains internal APIs for dataset pre-processing.

org.apache.ignite.ml.svm - package org.apache.ignite.ml.svm

Contains main APIs for SVM(support vector machines) algorithms.

org.apache.ignite.ml.trainers - package org.apache.ignite.ml.trainers

Contains model trainers.

org.apache.ignite.ml.trainers.transformers - package org.apache.ignite.ml.trainers.transformers

Various upstream transformers.

org.apache.ignite.ml.tree - package org.apache.ignite.ml.tree

Root package for decision trees.

org.apache.ignite.ml.tree.boosting - package org.apache.ignite.ml.tree.boosting

Contains implementation of gradient boosting on trees.

org.apache.ignite.ml.tree.data - package org.apache.ignite.ml.tree.data

Contains data and data builder required for decision tree trainers built on top of partition based dataset.

org.apache.ignite.ml.tree.impurity - package org.apache.ignite.ml.tree.impurity

Root package for decision tree impurity measures and calculators.

org.apache.ignite.ml.tree.impurity.gini - package org.apache.ignite.ml.tree.impurity.gini

Contains Gini impurity measure and calculator.

org.apache.ignite.ml.tree.impurity.mse - package org.apache.ignite.ml.tree.impurity.mse

Contains mean squared error impurity measure and calculator.

org.apache.ignite.ml.tree.impurity.util - package org.apache.ignite.ml.tree.impurity.util

Contains util classes used in decision tree impurity calculators.

org.apache.ignite.ml.tree.leaf - package org.apache.ignite.ml.tree.leaf

Root package for decision trees leaf builders.

org.apache.ignite.ml.tree.randomforest - package org.apache.ignite.ml.tree.randomforest

Contains random forest implementation classes.

org.apache.ignite.ml.tree.randomforest.data - package org.apache.ignite.ml.tree.randomforest.data

Package contains helper data structures for random forest implementation.

org.apache.ignite.ml.tree.randomforest.data.impurity - package org.apache.ignite.ml.tree.randomforest.data.impurity

Contains implementation of impurity computers based on histograms.

org.apache.ignite.ml.tree.randomforest.data.impurity.basic - package org.apache.ignite.ml.tree.randomforest.data.impurity.basic

Contains implementation of basic classes for impurity computers.

org.apache.ignite.ml.tree.randomforest.data.statistics - package org.apache.ignite.ml.tree.randomforest.data.statistics

Contains implementation of statistics computers for Random Forest.

org.apache.ignite.ml.util - package org.apache.ignite.ml.util

Contains some utils for ML module.

org.apache.ignite.ml.util.generators - package org.apache.ignite.ml.util.generators

Contains utility classes for data streams generation.

org.apache.ignite.ml.util.generators.primitives - package org.apache.ignite.ml.util.generators.primitives

Contains primitives like random scalars and random vector generators for composing own data stream generator.

org.apache.ignite.ml.util.generators.primitives.scalar - package org.apache.ignite.ml.util.generators.primitives.scalar

Contains generators of pseudo-random scalars in according to specific disctribution.

org.apache.ignite.ml.util.generators.primitives.vector - package org.apache.ignite.ml.util.generators.primitives.vector

Contains generators of pseudo-random vectors in according to specific disctribution.

org.apache.ignite.ml.util.generators.standard - package org.apache.ignite.ml.util.generators.standard

Contains classes for predefined data stream generators.

org.apache.ignite.ml.util.genetic - package org.apache.ignite.ml.util.genetic

Contains some genetic algorithms for discrete optimization task in ML module locally.

org.apache.ignite.ml.util.plugin - package org.apache.ignite.ml.util.plugin

Contains Ignite plugins system integration classes.

original - Variable in class org.apache.ignite.ml.preprocessing.developer.MappedPreprocessor

Original preprocessor.

outputSize() - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Size of output of MLP.

outputSupplier(IgniteFunction<A, B>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Transform function of form a -> b into a -> (() -> b).

outputSupplier(IgniteBiFunction<A, B, C>) - Static method in class org.apache.ignite.ml.math.functions.Functions

Transform function of form (a, b) -> c into (a, b) - () -> c.

P

p() - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationPreprocessor

Gets the degree of L^p space parameter value.

p() - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer

Gets the degree of L space parameter value.

parallelismStrategy() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Returns Parallelism Strategy instance.

ParallelismStrategy - Interface in org.apache.ignite.ml.environment.parallelism

Specifies the behaviour of processes in ML-algorithms that can may be parallelized such as parallel learning in bagging, learning submodels for One-vs-All model, Cross-Validation etc.

ParallelismStrategy.Type - Enum in org.apache.ignite.ml.environment.parallelism

The type of parallelism.

parallelogram(Vector) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of vectors from multidimension uniform distribution around zero.

parallelogram(Vector, long) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of vectors from multidimension uniform distribution around zero.

parameters() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get parameters vector.

parametersCount() - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Count of parameters in this MLP architecture.

parametersCount() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get count of parameters of this model.

ParameterSetGenerator - Class in org.apache.ignite.ml.selection.paramgrid

Generates tuples of hyper parameter values by given map.

ParameterSetGenerator(Map<Integer, Double[]>) - Constructor for class org.apache.ignite.ml.selection.paramgrid.ParameterSetGenerator

Creates an instance of the generator.

ParameterUpdateCalculator<M,P extends Serializable> - Interface in org.apache.ignite.ml.optimization.updatecalculators

Interface for classes encapsulating parameters updateCache logic.

ParametricVectorGenerator - Class in org.apache.ignite.ml.util.generators.primitives.vector

Generate vectors having components generated by parametrized function.

ParametricVectorGenerator(RandomProducer, IgniteFunction<Double, Double>...) - Constructor for class org.apache.ignite.ml.util.generators.primitives.vector.ParametricVectorGenerator

Create an intance of ParametricVectorGenerator.

Parametrized<M extends Parametrized<M>> - Interface in org.apache.ignite.ml.optimization

Interface for parametrized models.

paramGrid - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Parameter grid.

ParamGrid - Class in org.apache.ignite.ml.selection.paramgrid

Keeps the grid of parameters.

ParamGrid() - Constructor for class org.apache.ignite.ml.selection.paramgrid.ParamGrid

paramsAsVector(List<MLPLayer>) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Flatten this MLP parameters as vector.

parse(byte[]) - Method in class org.apache.ignite.ml.inference.parser.IgniteModelParser

Accepts serialized model represented by byte array, parses it and returns Model.

parse(byte[]) - Method in interface org.apache.ignite.ml.inference.parser.ModelParser

Accepts serialized model represented by byte array, parses it and returns Model.

partition(Object) - Method in class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

Returns key as a partition index.

partition() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Gets current partition.

PartitionContextBuilder<K,V,C extends Serializable> - Interface in org.apache.ignite.ml.dataset

Builder that accepts a partition upstream data and makes partition context.

PartitionDataBuilder<K,V,C extends Serializable,D extends AutoCloseable> - Interface in org.apache.ignite.ml.dataset

Builder that accepts a partition upstream data and partition context and makes partition data.

partitions() - Method in class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

parts - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Parts.

PatchedPreprocessor<K,V,L1,L2> - Class in org.apache.ignite.ml.preprocessing.developer

Preprocessing function that makes binarization.

PatchedPreprocessor(IgniteFunction<LabeledVector<L1>, LabeledVector<L2>>, Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.developer.PatchedPreprocessor

Constructs a new instance of Binarization preprocessor.

Pipeline<K,V,C extends Serializable,L> - Class in org.apache.ignite.ml.pipeline

A simple pipeline, which acts as a global trainer which produce a Pipeline Model.

Pipeline() - Constructor for class org.apache.ignite.ml.pipeline.Pipeline

pipeline - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Pipeline.

PipelineMdl<K,V> - Class in org.apache.ignite.ml.pipeline

Wraps the model produced by Pipeline.

PipelineMdl() - Constructor for class org.apache.ignite.ml.pipeline.PipelineMdl

plugin() - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

plus(H) - Method in interface org.apache.ignite.ml.dataset.feature.Histogram

plus(ObjectHistogram<T>) - Method in class org.apache.ignite.ml.dataset.feature.ObjectHistogram

PLUS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a + b.

plus(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns a + b.

plus(double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix where each value is a sum of the corresponding value of this matrix and argument value.

plus(Matrix) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix where each value is a sum of corresponding values of this matrix and passed in argument matrix.

plus(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix where each value is a sum of the corresponding value of this matrix and argument value.

plus(Matrix) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix where each value is a sum of corresponding values of this matrix and passed in argument matrix.

plus(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing sum of each element in this vector and argument.

plus(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Creates new vector containing element by element sum from both vectors.

plus(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing sum of each element in this vector and argument.

plus(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Creates new vector containing element by element sum from both vectors.

plus(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing sum of each element in this vector and argument.

plus(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Creates new vector containing element by element sum from both vectors.

plus(GiniHistogram) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.GiniHistogram

plus(ImpurityHistogramsComputer.NodeImpurityHistograms<S>) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.ImpurityHistogramsComputer.NodeImpurityHistograms

Store features statistics from other instance.

plus(MSEHistogram) - Method in class org.apache.ignite.ml.tree.randomforest.data.impurity.MSEHistogram

plus(NormalDistributionStatistics) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

Returns plus of normal distribution statistics.

plus(VectorGenerator) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Creates new generator by sum of vectors of this generator and other.

PLUS_ABS - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns Math.abs(a) + Math.abs(b).

plusMult(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns a + b*constant.

PointWithDistance<L> - Class in org.apache.ignite.ml.knn.utils

Utils class to be used in heap to compare two point using their distances to target point.

PointWithDistance(LabeledVector<L>, double) - Constructor for class org.apache.ignite.ml.knn.utils.PointWithDistance

Constructs a new instance of data point with distance.

PointWithDistanceUtil - Class in org.apache.ignite.ml.knn.utils

Util class with method that help working with PointWithDistance.

PointWithDistanceUtil() - Constructor for class org.apache.ignite.ml.knn.utils.PointWithDistanceUtil

Population - Class in org.apache.ignite.ml.util.genetic

Represents a populations of chromosomes.

Population(int) - Constructor for class org.apache.ignite.ml.util.genetic.Population

POPULATION_CACHE - Static variable in interface org.apache.ignite.ml.genetic.parameter.GAGridConstants

populationCache constant

populationCache() - Static method in class org.apache.ignite.ml.genetic.cache.PopulationCacheConfig

PopulationCacheConfig - Class in org.apache.ignite.ml.genetic.cache

Cache configuration for GAGridConstants.POPULATION_CACHE cache population of chromosomes (ie: potential solutions)

PopulationCacheConfig() - Constructor for class org.apache.ignite.ml.genetic.cache.PopulationCacheConfig

positiveClsLb() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

positiveClsLb() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Get the positive label.

pow(double) - Static method in class org.apache.ignite.ml.math.functions.Functions

Function that returns Math.pow(a, b).

precision - Variable in class org.apache.ignite.ml.composition.boosting.convergence.ConvergenceCheckerFactory

Precision of error checking.

precision() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Precision or Positive Predictive Value (PPV).

Precision<L> - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Precision calculator.

Precision(L) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.Precision

The class of interest or positive class.

predict(Vector) - Method in class org.apache.ignite.ml.clustering.gmm.GmmModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.composition.bagging.BaggedModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer.GDBModel

Applies containing models to features and aggregate them to one prediction.

predict(I) - Method in class org.apache.ignite.ml.composition.combinators.parallel.ModelsParallelComposition

Make a prediction for the specified input arguments.

predict(I) - Method in class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.composition.ModelOnFeaturesSubspace

Projects features vector to subspace in according to mapping and apply model to it.

predict(Vector) - Method in class org.apache.ignite.ml.composition.ModelsComposition

Applies containing models to features and aggregate them to one prediction.

predict(IS) - Method in class org.apache.ignite.ml.composition.stacking.StackedModel

Make a prediction for the specified input arguments.

predict(I) - Method in interface org.apache.ignite.ml.inference.Model

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.knn.classification.KNNClassificationModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.knn.regression.KNNRegressionModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

predict(Vector) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

Returns a number of class to which the input belongs.

predict(Matrix) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Makes a prediction for the given objects.

predict(Vector) - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

predict(ObjectSubjectPair<O, S>) - Method in class org.apache.ignite.ml.recommendation.RecommendationModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Make a prediction for the specified input arguments.

predict(String, Double...) - Static method in class org.apache.ignite.ml.sql.SQLFunctions

Makes prediction using specified model name to extract model from model storage and specified input values as input object for prediction.

predict(Vector) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Make a prediction for the specified input arguments.

predict(I) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Result of this model application is a result of composition before `andThen` inner mdl `andThen` after.

predict(Vector) - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.tree.DecisionTreeLeafNode

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

Make a prediction for the specified input arguments.

predict(Vector) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeRoot

Make a prediction for the specified input arguments.

predictionsAggregator() - Method in class org.apache.ignite.ml.composition.ModelsCompositionFormat

PredictionsAggregator - Interface in org.apache.ignite.ml.composition.predictionsaggregator

Predictions aggregator interface.

predictNextNodeKey(Vector) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

Returns leaf node for feature vector in according to decision tree.

predictRecommendation(String, Integer, Integer) - Static method in class org.apache.ignite.ml.sql.SQLFunctions

Makes prediction using specified model name to extract model from model storage and specified input values as input object for prediction.

PreprocessingTrainer<K,V> - Interface in org.apache.ignite.ml.preprocessing

Trainer for preprocessor.

Preprocessor<K,V> - Interface in org.apache.ignite.ml.preprocessing

Basic interface in Preprocessor Hierarchy.

preprocessor - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Preprocessor.

prevIterationGradient - Variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Previous iteration model partial derivatives by parameters.

prevIterationUpdates - Variable in class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Previous step weights updates.

prevIterationUpdates() - Method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Get previous step parameters updates.

prevIterationUpdates - Variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Previous iteration parameters updates.

printTree(DecisionTreeNode, boolean) - Static method in class org.apache.ignite.ml.tree.DecisionTree

Represents DecisionTree as String.

prob(Vector) - Method in interface org.apache.ignite.ml.math.stat.Distribution

prob(Vector) - Method in class org.apache.ignite.ml.math.stat.DistributionMixture

prob(Vector) - Method in class org.apache.ignite.ml.math.stat.MultivariateGaussianDistribution

ProbableLabel - Class in org.apache.ignite.ml.knn.ann

The special class for fuzzy labels presenting the probability distribution over the class labels.

ProbableLabel(TreeMap<Double, Double>) - Constructor for class org.apache.ignite.ml.knn.ann.ProbableLabel

The key is class label, the value is the probability to be an item of this class.

PROCESSOR_ID - Static variable in class org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor

Processor id.

Promise<T> - Interface in org.apache.ignite.ml.environment.parallelism

Future interface extension for lambda-friendly interface.

provideDiscoveryData(UUID) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

put(String, ModelDescriptor) - Method in class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier.

put(String, ModelDescriptor) - Method in class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier.

put(String, ModelDescriptor) - Method in interface org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier.

put(String, FileOrDirectory) - Method in class org.apache.ignite.ml.inference.storage.model.IgniteModelStorageProvider

Saves file or directory associated with the specified path.

put(String, FileOrDirectory) - Method in class org.apache.ignite.ml.inference.storage.model.LocalModelStorageProvider

Saves file or directory associated with the specified path.

put(String, FileOrDirectory) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorageProvider

Saves file or directory associated with the specified path.

putDistanceIdxPair(Map<Double, Set<Integer>>, int, double) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

putFile(String, byte[], boolean) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Creates a new or replaces existing file.

putFile(String, byte[], boolean) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Creates a new or replaces existing file.

putFile(String, byte[]) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Creates a new or replaces existing file.

putIfAbsent(String, ModelDescriptor) - Method in class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier if it's not saved yet.

putIfAbsent(String, ModelDescriptor) - Method in class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier if it's not saved yet.

putIfAbsent(String, ModelDescriptor) - Method in interface org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorage

Saves the specified model descriptor with the specified model identifier if it's not saved yet.

R

r2() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Returns coefficient of determination.

RANDOM_ACCESS_MODE - Static variable in interface org.apache.ignite.ml.math.StorageConstants

Storage mode optimized for random access.

randomDistribution(int) - Static method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Generates pseudorandom discrete distribution.

randomDistribution(int, long) - Static method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Generates pseudorandom discrete distribution.

RandomForestClassifierTrainer - Class in org.apache.ignite.ml.tree.randomforest

Classifier trainer based on RandomForest algorithm.

RandomForestClassifierTrainer(List<FeatureMeta>) - Constructor for class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Constructs an instance of RandomForestClassifierTrainer.

RandomForestRegressionTrainer - Class in org.apache.ignite.ml.tree.randomforest

Regression trainer based on RandomForest algorithm.

RandomForestRegressionTrainer(List<FeatureMeta>) - Constructor for class org.apache.ignite.ml.tree.randomforest.RandomForestRegressionTrainer

Constructs an instance of RandomForestRegressionTrainer.

RandomForestTrainer<L,S extends ImpurityComputer<BootstrappedVector,S>,T extends RandomForestTrainer<L,S,T>> - Class in org.apache.ignite.ml.tree.randomforest

Class represents a realization of Random Forest algorithm.

RandomForestTrainer(List<FeatureMeta>) - Constructor for class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Create an instance of RandomForestTrainer.

RandomInitializer - Class in org.apache.ignite.ml.nn.initializers

Class for initialization of MLP parameters with random uniformly distributed numbers from -1 to 1.

RandomInitializer(Random) - Constructor for class org.apache.ignite.ml.nn.initializers.RandomInitializer

Construct RandomInitializer from given RNG.

RandomInitializer(long) - Constructor for class org.apache.ignite.ml.nn.initializers.RandomInitializer

Constructs RandomInitializer with the given seed.

RandomInitializer() - Constructor for class org.apache.ignite.ml.nn.initializers.RandomInitializer

Constructs RandomInitializer with random seed.

randomNumbersGenerator() - Method in interface org.apache.ignite.ml.environment.LearningEnvironment

Random numbers generator.

RandomProducer - Interface in org.apache.ignite.ml.util.generators.primitives.scalar

Represents a generator of preudorandom scalar values.

RandomStrategy - Class in org.apache.ignite.ml.selection.paramgrid

This strategy enables the random search in hyper-parameter space.

RandomStrategy() - Constructor for class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

rawData() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

rawData() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

rawData() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

rawData() - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

read() - Method in class org.apache.ignite.ml.inference.reader.FileSystemModelReader

Rads model and returns it in serialized form as byte array.

read() - Method in class org.apache.ignite.ml.inference.reader.InMemoryModelReader

Rads model and returns it in serialized form as byte array.

read() - Method in interface org.apache.ignite.ml.inference.reader.ModelReader

Rads model and returns it in serialized form as byte array.

read() - Method in class org.apache.ignite.ml.inference.reader.ModelStorageModelReader

Rads model and returns it in serialized form as byte array.

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.distances.HammingDistance

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.structures.Dataset

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.structures.DatasetRow

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.structures.FeatureMetadata

readExternal(ObjectInput) - Method in class org.apache.ignite.ml.structures.LabeledVector

recall() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Sensitivity or True Positive Rate (TPR).

Recall<L> - Class in org.apache.ignite.ml.selection.scoring.metric.classification

Recall calculator.

Recall(L) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.Recall

The class of interest or positive class.

receiveDiscoveryData(UUID, Serializable) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

RecommendationBinaryDatasetDataBuilder - Class in org.apache.ignite.ml.recommendation.util

Recommendation binary dataset data builder.

RecommendationBinaryDatasetDataBuilder(String, String, String) - Constructor for class org.apache.ignite.ml.recommendation.util.RecommendationBinaryDatasetDataBuilder

Constructs a new instance of recommendation binary dataset data builder.

RecommendationDatasetData<O extends Serializable,S extends Serializable> - Class in org.apache.ignite.ml.recommendation.util

A partition data of a dataset required in RecommendationTrainer.

RecommendationDatasetData(List<? extends ObjectSubjectRatingTriplet<O, S>>) - Constructor for class org.apache.ignite.ml.recommendation.util.RecommendationDatasetData

Constructs a new instance of recommendation dataset data.

RecommendationDatasetDataBuilder<K,O extends Serializable,S extends Serializable,Z extends ObjectSubjectRatingTriplet<O,S>> - Class in org.apache.ignite.ml.recommendation.util

A partition data builder that makes SimpleDatasetData.

RecommendationDatasetDataBuilder() - Constructor for class org.apache.ignite.ml.recommendation.util.RecommendationDatasetDataBuilder

RecommendationModel<O extends Serializable,S extends Serializable> - Class in org.apache.ignite.ml.recommendation

Recommendation model that predicts rating for ObjectSubjectPair.

RecommendationModel(Map<O, Vector>, Map<S, Vector>) - Constructor for class org.apache.ignite.ml.recommendation.RecommendationModel

Constructs a new instance of recommendation model.

RecommendationTrainer - Class in org.apache.ignite.ml.recommendation

Trainer of the recommendation system.

RecommendationTrainer() - Constructor for class org.apache.ignite.ml.recommendation.RecommendationTrainer

reduce(List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.CrossOverTask

reduce(List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.FitnessTask

reduce(List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.MutateTask

reduce(List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.RouletteWheelSelectionTask

Return list of parent Chromosomes.

reduce(List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.TruncateSelectionTask

reduceStats(List<NormalDistributionStatistics>, List<NormalDistributionStatistics>, List<FeatureMeta>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatisticsComputer

Merges statistics on features from two partitions.

RegressionDataStream - Class in org.apache.ignite.ml.util.generators.standard

Represents a generator of regression data stream based on Vector->Double function where each Vector was produced from hypercube with sides = [minXValue, maxXValue].

RegressionDataStream(int, IgniteFunction<Vector, Double>, double, double) - Constructor for class org.apache.ignite.ml.util.generators.standard.RegressionDataStream

Creates an instance of RegressionDataStream.

RegressionLeafValuesComputer - Class in org.apache.ignite.ml.tree.randomforest.data.statistics

Implementation of LeafValuesComputer for regression task.

RegressionLeafValuesComputer() - Constructor for class org.apache.ignite.ml.tree.randomforest.data.statistics.RegressionLeafValuesComputer

RegressionMetrics - Class in org.apache.ignite.ml.selection.scoring.metric.regression

Regression metrics calculator.

RegressionMetrics() - Constructor for class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetrics

RegressionMetricValues - Class in org.apache.ignite.ml.selection.scoring.metric.regression

Provides access to regression metric values.

RegressionMetricValues(int, double, double, double) - Constructor for class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Initalize an instance.

RELU - Static variable in class org.apache.ignite.ml.nn.Activators

Rectified linear unit (ReLU) activation function.

remove(String) - Method in class org.apache.ignite.ml.inference.storage.descriptor.IgniteModelDescriptorStorage

Removes model descriptor for the specified model descriptor.

remove(String) - Method in class org.apache.ignite.ml.inference.storage.descriptor.LocalModelDescriptorStorage

Removes model descriptor for the specified model descriptor.

remove(String) - Method in interface org.apache.ignite.ml.inference.storage.descriptor.ModelDescriptorStorage

Removes model descriptor for the specified model descriptor.

remove(String) - Method in class org.apache.ignite.ml.inference.storage.model.DefaultModelStorage

Removes specified directory or file.

remove(String) - Method in class org.apache.ignite.ml.inference.storage.model.IgniteModelStorageProvider

Removes file or directory associated with the specified path.

remove(String) - Method in class org.apache.ignite.ml.inference.storage.model.LocalModelStorageProvider

Removes file or directory associated with the specified path.

remove(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorage

Removes specified directory or file.

remove(String) - Method in interface org.apache.ignite.ml.inference.storage.model.ModelStorageProvider

Removes file or directory associated with the specified path.

removeAttribute(String) - Method in interface org.apache.ignite.ml.math.MetaAttributes

Removes meta attribute with given name.

removeData(Ignite, UUID) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Remove data from local cache by Dataset ID.

removeEldestEntry(Map.Entry<K, V>) - Method in class org.apache.ignite.ml.util.LRUCache

removeLearningEnv(Ignite, UUID) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Remove learning environment from local cache by Dataset ID.

removeModel(Ignite, String) - Static method in class org.apache.ignite.ml.inference.IgniteModelStorageUtil

Removes model with specified name.

removeNode(UUID) - Method in class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

reset() - Method in class org.apache.ignite.ml.dataset.impl.cache.util.DatasetAffinityFunctionWrapper

resetProbabilitiesSettings() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Sets default settings equiprobableClasses to false and removes priorProbabilities.

resetSettings() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Sets default settings.

result(ComputeJobResult, List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.CrossOverTask

result(ComputeJobResult, List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.FitnessTask

result(ComputeJobResult, List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.MutateTask

result(ComputeJobResult, List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.RouletteWheelSelectionTask

result(ComputeJobResult, List<ComputeJobResult>) - Method in class org.apache.ignite.ml.genetic.TruncateSelectionTask

ring(double, double, double) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of 2D-vectors from ring-like distribution.

ring(double, double, double, long) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

Returns vector generator of 2D-vectors from ring-like distribution around zero.

RingsDataStream - Class in org.apache.ignite.ml.util.generators.standard

Represents a data stream of vectors produced by family of ring-like distributions around zero blurred by gauss distribution.

RingsDataStream(int, double, double) - Constructor for class org.apache.ignite.ml.util.generators.standard.RingsDataStream

Create an intance of RingsDataStream.

RingsDataStream(int, double, double, long) - Constructor for class org.apache.ignite.ml.util.generators.standard.RingsDataStream

Create an intance of RingsDataStream.

rmse() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Returns root mean squared error.

rocauc() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns ROCAUC value.

ROCAUC - Class in org.apache.ignite.ml.selection.scoring.metric.classification

ROC AUC score calculator.

ROCAUC() - Constructor for class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

rotate(double) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Rotate first two components of all vectors of generator by angle around zero.

rotate(double, int, int) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Rotate selected two components of all vectors of generator by angle around zero.

RouletteWheelSelectionJob - Class in org.apache.ignite.ml.genetic

Responsible for performing Roulette Wheel selection

RouletteWheelSelectionJob(Double, LinkedHashMap<Long, Double>) - Constructor for class org.apache.ignite.ml.genetic.RouletteWheelSelectionJob

RouletteWheelSelectionTask - Class in org.apache.ignite.ml.genetic

Responsible for performing Roulette Wheel selection.

RouletteWheelSelectionTask(GAConfiguration) - Constructor for class org.apache.ignite.ml.genetic.RouletteWheelSelectionTask

row() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix.Element

Gets element's row index.

ROW_STORAGE_MODE - Static variable in interface org.apache.ignite.ml.math.StorageConstants

Storage mode optimized for row access.

rowCount() - Method in class org.apache.ignite.ml.clustering.gmm.CovarianceMatricesAggregator

rowCountForNewCluster() - Method in class org.apache.ignite.ml.clustering.gmm.NewComponentStatisticsAggregator

RowIndexException - Exception in org.apache.ignite.ml.math.exceptions

This exception is used to indicate any error condition accessing matrix elements by invalid row index.

RowIndexException(int) - Constructor for exception org.apache.ignite.ml.math.exceptions.RowIndexException

rowOffset() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

rowsCount() - Method in class org.apache.ignite.ml.tree.data.TreeDataIndex

rowsCount(DecisionTreeData, TreeDataIndex) - Method in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Returns rows count in current dataset.

rowSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets number of rows in this matrix.

rowSize() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets number of rows in this matrix.

rowSize() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

rowSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

rowSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

rowSize() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

rowSize - Variable in class org.apache.ignite.ml.structures.Dataset

Amount of instances.

rowSize() - Method in class org.apache.ignite.ml.structures.Dataset

Gets amount of observation.

rowsLength() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

RPropParameterUpdate - Class in org.apache.ignite.ml.optimization.updatecalculators

Data needed for RProp updater.

RPropParameterUpdate(Vector, Vector, Vector, Vector) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Construct instance of this class by given parameters.

RPropUpdateCalculator - Class in org.apache.ignite.ml.optimization.updatecalculators

Class encapsulating RProp algorithm.

RPropUpdateCalculator(double, double, double) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Construct RPropUpdateCalculator.

RPropUpdateCalculator() - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Construct RPropUpdateCalculator with default parameters.

rss() - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetricValues

Returns residual sum of squares.

run() - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

The main method for genetic algorithm.

runParallel(LearningEnvironment) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

The main method for genetic algorithm.

S

sampleSize - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sample size.

SandboxMLCache - Class in org.apache.ignite.ml.util

Common utility code used in some ML examples to set up test cache.

SandboxMLCache(Ignite) - Constructor for class org.apache.ignite.ml.util.SandboxMLCache

save(D, P) - Method in interface org.apache.ignite.ml.Exporter

Save model by path p

save(D, String) - Method in class org.apache.ignite.ml.FileExporter

Save model by path p

saveAsCsv(Vector, String, String) - Static method in class org.apache.ignite.ml.math.Tracer

Saves given vector as CSV file.

saveAsCsv(Matrix, String, String) - Static method in class org.apache.ignite.ml.math.Tracer

Saves given matrix as CSV file.

saveContext(Ignite, String, int, C) - Static method in class org.apache.ignite.ml.dataset.impl.cache.util.ComputeUtils

Saves the specified partition context into the Ignite Cache.

saveModel(Exporter<KMeansModelFormat, P>, P) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

Save model by the given path.

saveModel(Exporter<ModelsCompositionFormat, P>, P) - Method in class org.apache.ignite.ml.composition.ModelsComposition

Save model by the given path.

saveModel(Exporter<D, P>, P) - Method in interface org.apache.ignite.ml.Exportable

Save model by the given path.

saveModel(Ignite, IgniteModel<I, O>, String) - Static method in class org.apache.ignite.ml.inference.IgniteModelStorageUtil

Saved specified model with specified name.

saveModel(Exporter<KNNModelFormat, P>, P) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

saveModel(Exporter<KNNModelFormat, P>, P) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Save model by the given path.

saveModel(Exporter<MultiClassModel, P>, P) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

Save model by the given path.

saveModel(Exporter<DiscreteNaiveBayesModel, P>, P) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesModel

Save model by the given path.

saveModel(Exporter<GaussianNaiveBayesModel, P>, P) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesModel

Save model by the given path.

saveModel(Exporter<LinearRegressionModel, P>, P) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

Save model by the given path.

saveModel(Exporter<LogisticRegressionModel, P>, P) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Save model by the given path.

saveModel(Exporter<SVMLinearClassificationModel, P>, P) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Save model by the given path.

scal(Double, Vector) - Static method in class org.apache.ignite.ml.math.Blas

Performs in-place multiplication of vector x by a real scalar a.

score(Function<IgniteBiPredicate<K, V>, DatasetBuilder<K, V>>, BiFunction<IgniteBiPredicate<K, V>, M, LabelPairCursor<L>>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Computes cross-validated metrics.

score(Iterator<LabelPair<Double>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics

Calculates score.

score(Iterator<LabelPair<L>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Accuracy

Calculates score.

score(Iterator<LabelPair<L>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Fmeasure

Calculates score.

score(Iterator<LabelPair<L>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Precision

Calculates score.

score(Iterator<LabelPair<L>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.Recall

Calculates score.

score(Iterator<LabelPair<Double>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Calculates score.

score(Iterator<LabelPair<L>>) - Method in interface org.apache.ignite.ml.selection.scoring.metric.Metric

Calculates score.

scoreAll(Iterator<LabelPair<Double>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics

Calculates metrics values.

scoreAll(Iterator<LabelPair<Double>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

Calculates binary metrics values.

scoreAll(Iterator<LabelPair<Double>>) - Method in class org.apache.ignite.ml.selection.scoring.metric.regression.RegressionMetrics

Calculates regression metrics values.

scoreByFolds() - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Calculates score by folds.

scoreByFolds() - Method in class org.apache.ignite.ml.selection.cv.CrossValidation

Calculates score by folds.

scoreByFolds() - Method in class org.apache.ignite.ml.selection.cv.DebugCrossValidation

Calculates score by folds.

scorePipeline(Function<IgniteBiPredicate<K, V>, DatasetBuilder<K, V>>, BiFunction<IgniteBiPredicate<K, V>, M, LabelPairCursor<L>>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Computes cross-validated metrics.

secondModel() - Method in class org.apache.ignite.ml.composition.combinators.sequential.ModelsSequentialComposition

Get second model.

selectBestKChromosome(int) - Method in class org.apache.ignite.ml.util.genetic.Population

Selects the top K chromosomes by fitness value from the smallest to the largest.

SelectionStrategy - Enum in org.apache.ignite.ml.util.genetic

Please, have a look at https://en.wikipedia.org/wiki/Selection_(genetic_algorithm).

selectKDistinct(int, int, Random) - Static method in class org.apache.ignite.ml.util.Utils

Select k distinct integers from range [0, n) with reservoir sampling: https://en.wikipedia.org/wiki/Reservoir_sampling.

selectKDistinct(int, int) - Static method in class org.apache.ignite.ml.util.Utils

Select k distinct integers from range [0, n) with reservoir sampling: https://en.wikipedia.org/wiki/Reservoir_sampling.

self() - Method in class org.apache.ignite.ml.knn.classification.KNNClassificationTrainer

Returns this instance.

self() - Method in class org.apache.ignite.ml.knn.KNNTrainer

Returns this instance.

self() - Method in class org.apache.ignite.ml.knn.regression.KNNRegressionTrainer

Returns this instance.

SEQUENTIAL_ACCESS_MODE - Static variable in interface org.apache.ignite.ml.math.StorageConstants

Storage mode optimized for sequential access.

serialize(Path) - Static method in class org.apache.ignite.ml.inference.util.DirectorySerializer

Serializes directory content.

serialize(T) - Static method in class org.apache.ignite.ml.util.Utils

Serialized the specified object.

serialVersionUID - Static variable in class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor

set(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Sets given value.

set(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix.Element

Sets element's value.

set(int, int, double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Sets given value.

set(int, int, double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

set(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

set(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

set(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sets value.

set(String, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

Sets element with specified string index and value.

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Sets value.

set(String, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.NamedVector

Sets element with specified string index and value.

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

set(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

set(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector.Element

Sets element's value.

set(int, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Sets value.

set(int, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

set(int, double) - Method in class org.apache.ignite.ml.structures.DatasetRow

Sets value.

setAttribute(String, T) - Method in interface org.apache.ignite.ml.math.MetaAttributes

Sets meta attribute with given name and value.

setBestHyperParams(Map<String, Double>) - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

Helper method in cross-validation process.

setBias(int, int, double) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Set the bias of given neuron in given layer.

setBiases(int, Vector) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Sets the biases of layer with a given index.

setBucketSize(double) - Method in class org.apache.ignite.ml.dataset.feature.BucketMeta

setBucketThresholds(double[][]) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Sets buckest borders.

setChromosome(int, Chromosome) - Method in class org.apache.ignite.ml.util.genetic.Population

Sets the chromsome for given index.

setChromosomeCriteria(ChromosomeCriteria) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

set value for ChromosomeCriteria

setChromosomeLen(int) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the Chromsome length

setCntOfValues(long) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

setColumn(int, double[]) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Sets values for given column.

setColumn(int, double[]) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Sets values for given column.

setCopiedOriginalLabels(double[]) - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

setCriteria(List<String>) - Method in class org.apache.ignite.ml.genetic.parameter.ChromosomeCriteria

Set criteria

setCrossOverRate(double) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the cross over rate.

setData(Row[]) - Method in class org.apache.ignite.ml.structures.Dataset

setDeltas(Vector) - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Set previous iteration deltas.

setDistributed(boolean) - Method in class org.apache.ignite.ml.structures.Dataset

setElitismCnt(int) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the elitism count.

setElseNode(DecisionTreeNode) - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

setEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.pipeline.Pipeline

Set learning environment builder.

setFitness(Double) - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Sets the fitness value.

setFitness(Integer, Double) - Method in class org.apache.ignite.ml.util.genetic.Population

Sets the fitness value for chromosome with the given index.

setFitnessFunction(IFitnessFunction) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set IFitnessFunction

setFitnessScore(double) - Method in class org.apache.ignite.ml.genetic.Chromosome

Set the fitnessScore for this chromosome

setGene(int, double) - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Sets gene value by index.

setGenePool(List<Gene>) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the gene pool.

setGenes(long[]) - Method in class org.apache.ignite.ml.genetic.Chromosome

Set the gene keys (ie: primary keys)

setImpurity(double) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

setLabel(L) - Method in class org.apache.ignite.ml.structures.LabeledVector

Set the label

setLabel(int, double) - Method in class org.apache.ignite.ml.structures.LabeledVectorSet

Fill the label with given value.

setLocScores(double[]) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation.TaskResult

setLog(IgniteLogger) - Method in class org.apache.ignite.ml.FileExporter

setMaxDepth(int) - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Set up the max depth.

setMaxDepth(int) - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Set up the max depth.

setMdlDescStorageBackups(Integer) - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

setMdlStorageBackups(Integer) - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

setMeta(FeatureMetadata[]) - Method in class org.apache.ignite.ml.structures.Dataset

setMinImpurityDecrease(double) - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Set up the min impurity decrease.

setMinImpurityDecrease(double) - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Set up the min impurity decrease.

setMinVal(double) - Method in class org.apache.ignite.ml.dataset.feature.BucketMeta

setMissingNode(DecisionTreeNode) - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

setMutationRate(double) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the mutation rate.

setName(String) - Method in class org.apache.ignite.ml.structures.FeatureMetadata

setParameters(Vector) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Set parameters.

setParamMap(Map<String, Double>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation.TaskResult

setPopulationSize(int) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the population size

setPreviousUpdates(Vector) - Method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Set previous step parameters updates.

setPriorProbabilities(double[]) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Sets prior probabilities.

setPriorProbabilities(double[]) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Sets prior probabilities.

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sets value.

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Sets value.

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

setRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

setRaw(Serializable) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector.Element

Sets any serializable object value.

setRaw(int, Serializable) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Sets value.

setRaw(int, Serializable) - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

setRawX(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sets value without checking for index boundaries.

setRawX(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Sets value without checking for index boundaries.

setRawX(int, Serializable) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Sets value without checking for index boundaries.

setRow(int, double[]) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Sets values for given row.

setRow(int, double[]) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Sets values for given row.

setSelectionMtd(GAGridConstants.SELECTION_METHOD) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the selection method

setStorage(MatrixStorage) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

setStorage(VectorStorage) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Set storage.

setSumOfValues(double) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.MeanValueStatistic

setTerminateCriteria(ITerminateCriteria) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set the termination criteria.

setThenNode(DecisionTreeNode) - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

setTruncateRate(double) - Method in class org.apache.ignite.ml.genetic.parameter.GAConfiguration

Set truncatePercentage

setU(double[]) - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRPartitionContext

setUpdatesMask(Vector) - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Set updates mask (values by which updateCache is multiplied).

setVal(Object) - Method in class org.apache.ignite.ml.genetic.Gene

Set the Gene value

setVal(double) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

setValuesForLeaves(ArrayList<TreeRoot>, Dataset<EmptyContext, BootstrappedDatasetPartition>) - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.LeafValuesComputer

Takes a list of all built trees and in one map-reduceImpurityStatistics step collect statistics for evaluating leaf-values for each tree and sets values for leaves.

setWeight(int, int, int, double) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Set the weight of neuron with given index in previous layer to neuron with given index in given layer.

setWeights(int, Matrix) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Sets the weighs of layer with a given index.

setWithMdlDescStorage(boolean) - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

setWithMdlStorage(boolean) - Method in class org.apache.ignite.ml.util.plugin.MLPluginConfiguration

setX(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Sets given value without checking for index bounds.

setX(int, int, double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Sets given value without checking for index bounds.

setX(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sets value without checking for index boundaries.

setX(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Sets value without checking for index boundaries.

setX(int, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Sets value without checking for index boundaries.

SHA256UniformMapper<K,V> - Class in org.apache.ignite.ml.selection.split.mapper

Implementation of uniform mappers based on SHA-256 hashing algorithm.

SHA256UniformMapper() - Constructor for class org.apache.ignite.ml.selection.split.mapper.SHA256UniformMapper

Constructs a new instance of SHA-256 uniform mapper.

SHA256UniformMapper(Random) - Constructor for class org.apache.ignite.ml.selection.split.mapper.SHA256UniformMapper

Constructs a new instance of SHA-256 uniform mapper.

showAscii(Vector, IgniteLogger, String) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Vector, IgniteLogger) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Vector, String) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Matrix) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Matrix, String) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Matrix, IgniteLogger, String) - Static method in class org.apache.ignite.ml.math.Tracer

showAscii(Vector) - Static method in class org.apache.ignite.ml.math.Tracer

showClassificationDatasetHtml(DataStreamGenerator, int, int, int, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Open browser and shows given dataset generator's data on two dimensional plane.

showClassificationDatasetHtml(String, DataStreamGenerator, int, int, int, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Open browser and shows given dataset generator's data on two dimensional plane.

showHtml(Matrix) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given matrix in the browser with D3-based visualization.

showHtml(Matrix, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given matrix in the browser with D3-based visualization.

showHtml(Matrix, Tracer.ColorMapper) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given matrix in the browser with D3-based visualization.

showHtml(Matrix, Tracer.ColorMapper, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given matrix in the browser with D3-based visualization.

showHtml(Vector) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given vector in the browser with D3-based visualization.

showHtml(Vector, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given vector in the browser with D3-based visualization.

showHtml(Vector, Tracer.ColorMapper) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given vector in the browser with D3-based visualization.

showHtml(Vector, Tracer.ColorMapper, boolean) - Static method in class org.apache.ignite.ml.math.Tracer

Shows given vector in the browser with D3-based visualization.

showRegressionDatasetInHtml(String, DataStreamGenerator, int, int) - Static method in class org.apache.ignite.ml.math.Tracer

Open browser and shows given dataset generator's data on two dimensional plane.

showRegressionDatasetInHtml(DataStreamGenerator, int, int) - Static method in class org.apache.ignite.ml.math.Tracer

Open browser and shows given dataset generator's data on two dimensional plane.

shuffle() - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Creates a permanent rearrangement mapping of features in vector and applies this rearrangement for each vectors of current generator.

shuffle(Long) - Method in interface org.apache.ignite.ml.util.generators.primitives.vector.VectorGenerator

Creates a permanent rearrangement mapping of features in vector and applies this rearrangement for each vectors of current generator.

SIGMOID - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns 1 / (1 + exp(-a)

SIGMOID - Static variable in class org.apache.ignite.ml.nn.Activators

Sigmoid activation function.

SIGMOIDGRADIENT - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a * (1-a)

SIGN - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns {@code a < 0 ?

SimpleDataset<C extends Serializable> - Class in org.apache.ignite.ml.dataset.primitive

A simple dataset introduces additional methods based on a matrix of features.

SimpleDataset(Dataset<C, SimpleDatasetData>) - Constructor for class org.apache.ignite.ml.dataset.primitive.SimpleDataset

Creates a new instance of simple dataset that introduces additional methods based on a matrix of features.

SimpleDatasetData - Class in org.apache.ignite.ml.dataset.primitive.data

A partition data of the SimpleDataset containing matrix of features in flat column-major format stored in heap.

SimpleDatasetData(double[], int) - Constructor for class org.apache.ignite.ml.dataset.primitive.data.SimpleDatasetData

Constructs a new instance of partition data of the SimpleDataset containing matrix of features in flat column-major format stored in heap.

SimpleDatasetDataBuilder<K,V,C extends Serializable,CO extends Serializable> - Class in org.apache.ignite.ml.dataset.primitive.builder.data

A partition data builder that makes SimpleDatasetData.

SimpleDatasetDataBuilder(Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.dataset.primitive.builder.data.SimpleDatasetDataBuilder

Construct a new instance of partition data builder that makes SimpleDatasetData.

SimpleGDParameterUpdate - Class in org.apache.ignite.ml.optimization.updatecalculators

Parameters for SimpleGDUpdateCalculator.

SimpleGDParameterUpdate(int) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate

Construct instance of this class.

SimpleGDParameterUpdate(Vector) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate

Construct instance of this class.

SimpleGDUpdateCalculator - Class in org.apache.ignite.ml.optimization.updatecalculators

Simple gradient descent parameters updater.

SimpleGDUpdateCalculator() - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Construct instance of this class with default parameters.

SimpleGDUpdateCalculator(double) - Constructor for class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Construct SimpleGDUpdateCalculator.

SimpleLabeledDataset<C extends Serializable> - Class in org.apache.ignite.ml.dataset.primitive

A simple labeled dataset introduces additional methods based on a matrix of features and labels vector.

SimpleLabeledDataset(Dataset<C, SimpleLabeledDatasetData>) - Constructor for class org.apache.ignite.ml.dataset.primitive.SimpleLabeledDataset

Creates a new instance of simple labeled dataset that introduces additional methods based on a matrix of features and labels vector.

SimpleLabeledDatasetData - Class in org.apache.ignite.ml.dataset.primitive.data

A partition data of the SimpleLabeledDataset containing matrix of features in flat column-major format stored in heap and vector of labels stored in heap as well.

SimpleLabeledDatasetData(double[], double[], int) - Constructor for class org.apache.ignite.ml.dataset.primitive.data.SimpleLabeledDatasetData

Constructs a new instance of partition data of the SimpleLabeledDataset containing matrix of features in flat column-major format stored in heap and vector of labels stored in heap as well.

SimpleLabeledDatasetDataBuilder<K,V,C extends Serializable> - Class in org.apache.ignite.ml.dataset.primitive.builder.data

A partition data builder that makes SimpleLabeledDatasetData.

SimpleLabeledDatasetDataBuilder(Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.dataset.primitive.builder.data.SimpleLabeledDatasetDataBuilder

Constructs a new instance of partition data builder that makes SimpleLabeledDatasetData.

SimpleStackedDatasetTrainer<I,O,AM extends IgniteModel<I,O>,L> - Class in org.apache.ignite.ml.composition.stacking

DatasetTrainer with same type of input and output of submodels.

SimpleStackedDatasetTrainer(DatasetTrainer<AM, L>, IgniteBinaryOperator<I>, IgniteFunction<I, I>, IgniteFunction<Vector, I>, IgniteFunction<I, Vector>) - Constructor for class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Construct instance of this class.

SimpleStackedDatasetTrainer(DatasetTrainer<AM, L>, IgniteBinaryOperator<I>) - Constructor for class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Construct instance of this class.

SimpleStackedDatasetTrainer() - Constructor for class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Constructs instance of this class.

SimpleStepFunctionCompressor<T extends ImpurityMeasure<T>> - Class in org.apache.ignite.ml.tree.impurity.util

Simple step function compressor.

SimpleStepFunctionCompressor() - Constructor for class org.apache.ignite.ml.tree.impurity.util.SimpleStepFunctionCompressor

Constructs a new instance of simple step function compressor with default parameters.

SimpleStepFunctionCompressor(int, double, double) - Constructor for class org.apache.ignite.ml.tree.impurity.util.SimpleStepFunctionCompressor

Constructs a new instance of simple step function compressor.

SingleLabelDatasetTrainer<M extends IgniteModel> - Class in org.apache.ignite.ml.trainers

Interface for trainers that trains on dataset with singe label per object.

SingleLabelDatasetTrainer() - Constructor for class org.apache.ignite.ml.trainers.SingleLabelDatasetTrainer

SingleModelBuilder - Class in org.apache.ignite.ml.inference.builder

Implementation of synchronous inference model builder that builds a model processed locally in a single thread.

SingleModelBuilder() - Constructor for class org.apache.ignite.ml.inference.builder.SingleModelBuilder

SingularMatrixException - Exception in org.apache.ignite.ml.math.exceptions

Exception to be thrown when a non-singular matrix is expected.

SingularMatrixException() - Constructor for exception org.apache.ignite.ml.math.exceptions.SingularMatrixException

SingularMatrixException(String, Object...) - Constructor for exception org.apache.ignite.ml.math.exceptions.SingularMatrixException

size() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets cardinality of this vector (maximum number of the elements).

size() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets cardinality of this vector (maximum number of the elements).

size() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

size() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

size() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

size() - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

size() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets cardinality of this vector (maximum number of the elements).

size() - Method in interface org.apache.ignite.ml.math.primitives.vector.VectorStorage

size() - Method in class org.apache.ignite.ml.structures.DatasetRow

Gets cardinality of dataset row (maximum number of the elements).

size() - Method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

size() - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Returns the amount of genes in chromosome.

size() - Method in class org.apache.ignite.ml.util.genetic.Population

Returns the size of population.

sizeOf(K, V) - Method in class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.ArrayLikeVectorizer

Size of array-like structure of upstream object.

sizeOf(K, double[]) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DoubleArrayVectorizer

Size of array-like structure of upstream object.

sizeOf(K, Vector) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.DummyVectorizer

Size of array-like structure of upstream object.

SmoothParametrized<M extends Parametrized<M>> - Interface in org.apache.ignite.ml.optimization

Interface for models which are smooth functions of their parameters.

solve(double, double, double, double, double, boolean, double[]) - Method in class org.apache.ignite.ml.math.isolve.lsqr.AbstractLSQR

Solves given Sparse Linear Systems.

solve(Vector) - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

solve(Matrix) - Method in class org.apache.ignite.ml.math.primitives.matrix.LUDecomposition

sort() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sorts this vector in ascending order.

sort() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Sorts this vector in ascending order.

sort() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Sorts this vector in ascending order.

sort(int) - Method in class org.apache.ignite.ml.tree.data.DecisionTreeData

Sorts data by specified column in ascending order.

SparseMatrix - Class in org.apache.ignite.ml.math.primitives.matrix.impl

Sparse local onheap matrix with SparseVector as rows.

SparseMatrix() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

SparseMatrix(int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.SparseMatrix

Construct new SparseMatrix.

SparseMatrixStorage - Class in org.apache.ignite.ml.math.primitives.matrix.storage

Storage for sparse, local, on-heap matrix.

SparseMatrixStorage() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

SparseMatrixStorage(int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

SparseVector - Class in org.apache.ignite.ml.math.primitives.vector.impl

Local on-heap sparse vector based on hash map storage.

SparseVector() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

SparseVector(Map<Integer, Double>, boolean) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

SparseVector(int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

SparseVectorStorage - Class in org.apache.ignite.ml.math.primitives.vector.storage

Sparse, local, on-heap vector storage.

SparseVectorStorage() - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

SparseVectorStorage(Map<Integer, ? extends Serializable>, boolean) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

SparseVectorStorage(int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

SpatialIndex<L> - Interface in org.apache.ignite.ml.knn.utils.indices

An index that works with spatial data and allows to quickly find k closest element.

SpatialIndexType - Enum in org.apache.ignite.ml.knn.utils.indices

Spatial index type.

specificity() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Returns Specificity (SPC) or True Negative Rate (TNR).

split(double) - Method in class org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter

Splits dataset into train and test subsets.

split(double, double) - Method in class org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter

Splits dataset into train and test subsets.

split(TreeNode) - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeSplit

Split node from parameter onto two children nodes.

spr(Double, DenseVector, DenseVector) - Static method in class org.apache.ignite.ml.math.Blas

Adds alpha * v * v.t to a matrix in-place.

spr(Double, SparseVector, DenseVector) - Static method in class org.apache.ignite.ml.math.Blas

SqlDatasetBuilder - Class in org.apache.ignite.ml.sql

Subclass of CacheBasedDatasetBuilder that should be used to work with SQL tables.

SqlDatasetBuilder(Ignite, String) - Constructor for class org.apache.ignite.ml.sql.SqlDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset with default predicate that passes all upstream entries to dataset.

SqlDatasetBuilder(Ignite, String, IgniteBiPredicate<Object, BinaryObject>) - Constructor for class org.apache.ignite.ml.sql.SqlDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset.

SqlDatasetBuilder(Ignite, String, IgniteBiPredicate<Object, BinaryObject>, UpstreamTransformerBuilder) - Constructor for class org.apache.ignite.ml.sql.SqlDatasetBuilder

Constructs a new instance of cache based dataset builder that makes CacheBasedDataset.

SQLFunctions - Class in org.apache.ignite.ml.sql

SQL functions that should be defined and passed into cache configuration to extend list of functions available in SQL interface.

SQLFunctions() - Constructor for class org.apache.ignite.ml.sql.SQLFunctions

SQRT - Static variable in class org.apache.ignite.ml.tree.randomforest.data.FeaturesCountSelectionStrategies

SQUARE - Static variable in class org.apache.ignite.ml.math.functions.Functions

Function that returns a * a.

SquaredError - Class in org.apache.ignite.ml.composition.boosting.loss

Represent error function as E(label, modelAnswer) = 1/N * (label - prediction)^2

SquaredError() - Constructor for class org.apache.ignite.ml.composition.boosting.loss.SquaredError

StackedDatasetTrainer<IS,IA,O,AM extends IgniteModel<IA,O>,L> - Class in org.apache.ignite.ml.composition.stacking

DatasetTrainer encapsulating stacking technique for model training.

StackedDatasetTrainer(DatasetTrainer<AM, L>, IgniteBinaryOperator<IA>, IgniteFunction<IS, IA>, List<DatasetTrainer<IgniteModel<IS, IA>, L>>, IgniteFunction<Vector, IS>, IgniteFunction<IA, Vector>) - Constructor for class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Create instance of this class.

StackedDatasetTrainer(DatasetTrainer<AM, L>, IgniteBinaryOperator<IA>, IgniteFunction<IS, IA>) - Constructor for class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Constructs instance of this class.

StackedDatasetTrainer() - Constructor for class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Constructs instance of this class.

StackedModel<IS,IA,O,AM extends IgniteModel<IA,O>> - Class in org.apache.ignite.ml.composition.stacking

This is a wrapper for model produced by StackedDatasetTrainer.

StackedVectorDatasetTrainer<O,AM extends IgniteModel<Vector,O>,L> - Class in org.apache.ignite.ml.composition.stacking

StackedDatasetTrainer with Vector as submodels input and output.

StackedVectorDatasetTrainer(DatasetTrainer<AM, L>) - Constructor for class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Constructs instance of this class.

StackedVectorDatasetTrainer() - Constructor for class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Constructs instance of this class.

StandardScalerData - Class in org.apache.ignite.ml.preprocessing.standardscaling

A Service class for StandardScalerTrainer which used for sums holing.

StandardScalerData(double[], double[], long) - Constructor for class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerData

Creates StandardScalerData.

StandardScalerPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.standardscaling

The preprocessing function that makes standard scaling, transforms features to make mean equal to 0 and variance equal to 1.

StandardScalerPreprocessor(double[], double[], Preprocessor<K, V>) - Constructor for class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerPreprocessor

Constructs a new instance of standardscaling preprocessor.

StandardScalerTrainer<K,V> - Class in org.apache.ignite.ml.preprocessing.standardscaling

Trainer of the standard scaler preprocessor.

StandardScalerTrainer() - Constructor for class org.apache.ignite.ml.preprocessing.standardscaling.StandardScalerTrainer

start(PluginContext) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

std() - Method in class org.apache.ignite.ml.dataset.primitive.SimpleDataset

Calculates standard deviation by all columns.

std() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

StepFunction<T extends ImpurityMeasure<T>> - Class in org.apache.ignite.ml.tree.impurity.util

Step function described by x and y points.

StepFunction(double[], T[]) - Constructor for class org.apache.ignite.ml.tree.impurity.util.StepFunction

Constructs a new instance of step function.

StepFunctionCompressor<T extends ImpurityMeasure<T>> - Interface in org.apache.ignite.ml.tree.impurity.util

Base interface for step function compressors which reduces step function size.

stop(boolean) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

StorageConstants - Interface in org.apache.ignite.ml.math

Support for different modes of accessing data storage.

storageGet(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

storageGet(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

storageGetRaw(int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets serializable value from storage and casts it to targe type T.

storageMode() - Method in interface org.apache.ignite.ml.math.primitives.matrix.MatrixStorage

storageMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

storageMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

storageMode() - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

StorageOpsMetrics - Interface in org.apache.ignite.ml.math

Storage and operation cost characteristics.

storageSet(int, int, double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

storageSet(int, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

storageSetRaw(int, Serializable) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Sets serializable value.

StringCoordVectorizer(String...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.StringCoordVectorizer

Creates an instance of Vectorizer.

StringEncoderPreprocessor<K,V> - Class in org.apache.ignite.ml.preprocessing.encoding.stringencoder

Preprocessing function that makes String encoding.

StringEncoderPreprocessor(Map<String, Integer>[], Preprocessor<K, V>, Set<Integer>) - Constructor for class org.apache.ignite.ml.preprocessing.encoding.stringencoder.StringEncoderPreprocessor

Constructs a new instance of String Encoder preprocessor.

Stub(T) - Constructor for class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy.Stub

Create an instance of Stub

submit(IgniteSupplier<T>) - Method in class org.apache.ignite.ml.environment.parallelism.DefaultParallelismStrategy

Submit task.

submit(IgniteSupplier<T>) - Method in class org.apache.ignite.ml.environment.parallelism.NoParallelismStrategy

Submit task.

submit(IgniteSupplier<T>) - Method in interface org.apache.ignite.ml.environment.parallelism.ParallelismStrategy

Submit task.

submit(List<IgniteSupplier<T>>) - Method in interface org.apache.ignite.ml.environment.parallelism.ParallelismStrategy

Submit the list of tasks.

submodels() - Method in class org.apache.ignite.ml.composition.combinators.parallel.ModelsParallelComposition

List of submodels constituting this model.

subtract(GiniImpurityMeasure) - Method in class org.apache.ignite.ml.tree.impurity.gini.GiniImpurityMeasure

Subtracts the given impurity for this.

subtract(T) - Method in interface org.apache.ignite.ml.tree.impurity.ImpurityMeasure

Subtracts the given impurity for this.

subtract(MSEImpurityMeasure) - Method in class org.apache.ignite.ml.tree.impurity.mse.MSEImpurityMeasure

Subtracts the given impurity for this.

sum() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Gets sum of all elements in the matrix.

sum() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Gets sum of all elements in the matrix.

sum() - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets the sum of all elements in this vector.

sum() - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets the sum of all elements in this vector.

sum() - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets the sum of all elements in this vector.

sum(List<NesterovParameterUpdate>) - Static method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovParameterUpdate

Get sum of parameters updates.

SUM - Static variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Sums updates returned by different trainings.

SUM_LOCAL - Static variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Sums updates during one training.

SUM_LOCAL - Static variable in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDParameterUpdate

Method used to get total update of all parallel trainings.

sums() - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Gets the array of sums of values in partition for each feature in the dataset.

SVMLinearClassificationModel - Class in org.apache.ignite.ml.svm

Base class for SVM linear classification model.

SVMLinearClassificationModel(Vector, double) - Constructor for class org.apache.ignite.ml.svm.SVMLinearClassificationModel

SVMLinearClassificationTrainer - Class in org.apache.ignite.ml.svm

Base class for a soft-margin SVM linear classification trainer based on the communication-efficient distributed dual coordinate ascent algorithm (CoCoA) with hinge-loss function.

SVMLinearClassificationTrainer() - Constructor for class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

swapColumns(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Swaps two columns in this matrix.

swapColumns(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Swaps two columns in this matrix.

swapRows(int, int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Swaps two rows in this matrix.

swapRows(int, int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Swaps two rows in this matrix.

SyncModelBuilder - Interface in org.apache.ignite.ml.inference.builder

Builder of synchronous inference model.

syr(Double, SparseVector, DenseMatrix) - Static method in class org.apache.ignite.ml.math.Blas

T

TaskResult(Map<String, Double>, double[]) - Constructor for class org.apache.ignite.ml.selection.cv.AbstractCrossValidation.TaskResult

test() - Method in class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair

Test subset of the whole dataset.

ThreadedModelBuilder - Class in org.apache.ignite.ml.inference.builder

Implementation of asynchronous inference model builder that builds model processed locally utilizing specified number of threads.

ThreadedModelBuilder(int) - Constructor for class org.apache.ignite.ml.inference.builder.ThreadedModelBuilder

Constructs a new instance of threaded inference model builder.

threshold() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Gets the threshold.

threshold() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Gets the threshold.

times(double) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix containing the product of given value and values in this matrix.

times(Vector) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix that is the product of multiplying this matrix and the argument vector.

times(Matrix) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix that is the product of multiplying this matrix and the argument matrix.

times(double) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix containing the product of given value and values in this matrix.

times(Matrix) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix that is the product of multiplying this matrix and the argument matrix.

times(Vector) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix that is the product of multiplying this matrix and the argument vector.

times(double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets a new vector that contains product of each element and the argument.

times(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Gets a new vector that is an element-wie product of this vector and the argument.

times(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets a new vector that contains product of each element and the argument.

times(Vector) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Gets a new vector that is an element-wie product of this vector and the argument.

times(double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.SparseVector

Gets a new vector that contains product of each element and the argument.

times(double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets a new vector that contains product of each element and the argument.

times(Vector) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Gets a new vector that is an element-wie product of this vector and the argument.

tn() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

toConditional(int, double) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

Convert node to conditional node.

toDoubleArray() - Method in class org.apache.ignite.ml.util.genetic.Chromosome

Returns the double array chromosome representation.

toLeaf(double) - Method in class org.apache.ignite.ml.tree.randomforest.data.TreeNode

Convert node to leaf.

toMap() - Method in interface org.apache.ignite.ml.selection.scoring.metric.MetricValues

Returns the pair of metric name and metric value.

toMatrix(boolean) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Converts this vector into [N x 1] or [1 x N] matrix where N is this vector cardinality.

toMatrix(boolean) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Converts this vector into [N x 1] or [1 x N] matrix where N is this vector cardinality.

toMatrix(boolean) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Converts this vector into [N x 1] or [1 x N] matrix where N is this vector cardinality.

toMatrixPlusOne(boolean, double) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

Converts this vector into [N+1 x 1] or [1 x N+1] matrix where N is this vector cardinality

toMatrixPlusOne(boolean, double) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

Converts this vector into [N+1 x 1] or [1 x N+1] matrix where N is this vector cardinality

toMatrixPlusOne(boolean, double) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

Converts this vector into [N+1 x 1] or [1 x N+1] matrix where N is this vector cardinality

toString() - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

toString(boolean) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansModel

toString() - Method in class org.apache.ignite.ml.composition.ModelOnFeaturesSubspace

toString(boolean) - Method in class org.apache.ignite.ml.composition.ModelOnFeaturesSubspace

toString() - Method in class org.apache.ignite.ml.composition.ModelsComposition

toString(boolean) - Method in class org.apache.ignite.ml.composition.ModelsComposition

toString(boolean) - Method in interface org.apache.ignite.ml.composition.predictionsaggregator.PredictionsAggregator

Represents aggregator as String.

toString() - Method in class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

toString(boolean) - Method in class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

Represents aggregator as String.

toString() - Method in class org.apache.ignite.ml.genetic.Chromosome

toString() - Method in class org.apache.ignite.ml.genetic.Gene

toString(boolean) - Method in interface org.apache.ignite.ml.IgniteModel

toString() - Method in class org.apache.ignite.ml.inference.ModelDescriptor

toString() - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

toString(boolean) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationModel

toString() - Method in class org.apache.ignite.ml.knn.NNClassificationModel

toString(boolean) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

toString() - Method in class org.apache.ignite.ml.math.isolve.IterativeSolverResult

toString() - Method in class org.apache.ignite.ml.math.isolve.lsqr.LSQRResult

toString() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

toString() - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

toString(boolean) - Method in class org.apache.ignite.ml.multiclass.MultiClassModel

toString() - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

toString(boolean) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

toString() - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

toString() - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

toString(boolean) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionModel

toString() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

toString(boolean) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

toString() - Method in class org.apache.ignite.ml.selection.cv.CrossValidationResult

toString() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

toString(boolean) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

toString() - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

toString(boolean) - Method in class org.apache.ignite.ml.tree.DecisionTreeConditionalNode

toString() - Method in class org.apache.ignite.ml.tree.DecisionTreeLeafNode

toString(boolean) - Method in class org.apache.ignite.ml.tree.DecisionTreeLeafNode

toString() - Method in class org.apache.ignite.ml.util.ModelTrace

TotalCostAndCounts() - Constructor for class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer.TotalCostAndCounts

totalRowCount() - Method in class org.apache.ignite.ml.clustering.gmm.NewComponentStatisticsAggregator

tp() - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetricValues

Tracer - Class in org.apache.ignite.ml.math

Utility methods to support output of Vector and Matrix instances to plain text or HTML.

Tracer() - Constructor for class org.apache.ignite.ml.math.Tracer

Tracer.ColorMapper - Interface in org.apache.ignite.ml.math

Double to color mapper.

train() - Method in class org.apache.ignite.ml.structures.LabeledVectorSetTestTrainPair

Train subset of the whole dataset.

trainer - Variable in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Trainer.

trainerEnvironment - Variable in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Learning environment used for trainer.

TrainersParallelComposition<I,O,L> - Class in org.apache.ignite.ml.composition.combinators.parallel

This class represents a parallel composition of trainers.

TrainersParallelComposition(List<T>) - Constructor for class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

Construct an instance of this class from a list of trainers.

TrainersSequentialComposition<I,O1,O2,L> - Class in org.apache.ignite.ml.composition.combinators.sequential

Sequential composition of trainers.

TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I, O1>, L>, DatasetTrainer<? extends IgniteModel<O1, O2>, L>, IgniteFunction<? super IgniteModel<I, O1>, IgniteFunction<LabeledVector<L>, LabeledVector<L>>>) - Constructor for class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Construct sequential composition of given two trainers.

TrainersSequentialComposition(DatasetTrainer<? extends IgniteModel<I, O1>, L>, DatasetTrainer<? extends IgniteModel<O1, O2>, L>, IgniteBiFunction<Integer, ? super IgniteModel<I, O1>, IgniteFunction<LabeledVector<L>, LabeledVector<L>>>) - Constructor for class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Create sequential composition of two trainers.

TrainerTransformers - Class in org.apache.ignite.ml.trainers

Class containing various trainer transformers.

TrainerTransformers() - Constructor for class org.apache.ignite.ml.trainers.TrainerTransformers

TrainTestDatasetSplitter<K,V> - Class in org.apache.ignite.ml.selection.split

Dataset splitter that splits dataset into train and test subsets.

TrainTestDatasetSplitter() - Constructor for class org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter

Constructs a new instance of train test dataset splitter.

TrainTestDatasetSplitter(UniformMapper<K, V>) - Constructor for class org.apache.ignite.ml.selection.split.TrainTestDatasetSplitter

Constructs a new instance of train test dataset splitter.

TrainTestSplit<K,V> - Class in org.apache.ignite.ml.selection.split

Dataset split that encapsulates train and test subsets.

TrainTestSplit(IgniteBiPredicate<K, V>, IgniteBiPredicate<K, V>) - Constructor for class org.apache.ignite.ml.selection.split.TrainTestSplit

Constructs a new instance of train test split.

transfomToListOrdered(Queue<PointWithDistance<L>>) - Static method in class org.apache.ignite.ml.knn.utils.PointWithDistanceUtil

Util method that transforms collection of PointWithDistance to array of LabeledVector.

transform(Stream<UpstreamEntry>) - Method in interface org.apache.ignite.ml.dataset.UpstreamTransformer

Transform upstream.

transform(Stream<UpstreamEntry>) - Method in class org.apache.ignite.ml.trainers.transformers.BaggingUpstreamTransformer

Transform upstream.

transformationLayerArchitecture(int) - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Get architecture of transformation layer (i.e. non-input layer) with given index.

TransformationLayerArchitecture - Class in org.apache.ignite.ml.nn.architecture

Class encapsulation architecture of transformation layer (i.e. non-input layer).

TransformationLayerArchitecture(int, boolean, IgniteDifferentiableDoubleToDoubleFunction) - Constructor for class org.apache.ignite.ml.nn.architecture.TransformationLayerArchitecture

Construct TransformationLayerArchitecture.

transpose() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new matrix that is transpose of this matrix.

transpose() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new matrix that is transpose of this matrix.

TreeDataIndex - Class in org.apache.ignite.ml.tree.data

Index for representing sorted dataset rows for each features.

TreeDataIndex(double[][], double[]) - Constructor for class org.apache.ignite.ml.tree.data.TreeDataIndex

Constructs an instance of TreeDataIndex.

TreeFilter - Interface in org.apache.ignite.ml.tree

Predicate used to define objects that placed in decision tree node.

treeId() - Method in class org.apache.ignite.ml.tree.randomforest.data.NodeId

TreeNode - Class in org.apache.ignite.ml.tree.randomforest.data

Decision tree node class.

TreeNode(long, int) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.TreeNode

Create an instance of TreeNode.

TreeNode.Type - Enum in org.apache.ignite.ml.tree.randomforest.data

Type of node.

TreeRoot - Class in org.apache.ignite.ml.tree.randomforest.data

Tree root class.

TreeRoot(TreeNode, Set<Integer>) - Constructor for class org.apache.ignite.ml.tree.randomforest.data.TreeRoot

Create an instance of TreeRoot.

TruncateSelectionJob - Class in org.apache.ignite.ml.genetic

Responsible for performing truncate selection

TruncateSelectionJob(Long, List<Long>) - Constructor for class org.apache.ignite.ml.genetic.TruncateSelectionJob

TruncateSelectionTask - Class in org.apache.ignite.ml.genetic

Responsible for performing truncate selection.

TruncateSelectionTask(List<Long>, int) - Constructor for class org.apache.ignite.ml.genetic.TruncateSelectionTask

tryToAddIntoHeap(Queue<PointWithDistance<L>>, int, LabeledVector<L>, double) - Static method in class org.apache.ignite.ml.knn.utils.PointWithDistanceUtil

Util method that adds data point into heap if it fits (if heap size is less than k or a distance from taget point to data point is less than a distance from target point to the most distant data point in heap).

tryToAddIntoHeap(Queue<PointWithDistance<L>>, int, Vector, List<LabeledVector<L>>, DistanceMeasure) - Static method in class org.apache.ignite.ml.knn.utils.PointWithDistanceUtil

Util method that adds data points into heap if they fits (if heap size is less than k or a distance from taget point to data point is less than a distance from target point to the most distant data point in heap).

tuneHyperParamterers() - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Finds the best set of hyperparameters based on parameter search strategy.

twoDimensional(IgniteFunction<Double, Double>, double, double) - Static method in class org.apache.ignite.ml.util.generators.standard.RegressionDataStream

Creates two dimensional regression data stream.

twoDimensional(IgniteFunction<Double, Double>, double, double, long) - Static method in class org.apache.ignite.ml.util.generators.standard.RegressionDataStream

Creates two dimensional regression data stream.

TwoSeparableClassesDataStream - Class in org.apache.ignite.ml.util.generators.standard

2D-Vectors data stream with two separable classes.

TwoSeparableClassesDataStream(double, double) - Constructor for class org.apache.ignite.ml.util.generators.standard.TwoSeparableClassesDataStream

Create an instance of TwoSeparableClassesDataStream.

TwoSeparableClassesDataStream(double, double, long) - Constructor for class org.apache.ignite.ml.util.generators.standard.TwoSeparableClassesDataStream

Create an instance of TwoSeparableClassesDataStream.

U

unflatten(double[], int, int) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

unflatten(double[], Matrix) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

uniform(int) - Static method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Creates a producer of random values from uniform discrete distribution.

uniform(int, long) - Static method in class org.apache.ignite.ml.util.generators.primitives.scalar.DiscreteRandomProducer

Creates a producer of random values from uniform discrete distribution.

UniformMapper<K,V> - Interface in org.apache.ignite.ml.selection.split.mapper

Interface for util mappers that maps a key-value pair to a point on the segment (0, 1).

UniformRandomProducer - Class in org.apache.ignite.ml.util.generators.primitives.scalar

Pseudorandom producer generating values from uniform continuous distribution.

UniformRandomProducer(double, double) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.UniformRandomProducer

Creates an instance of UniformRandomProducer.

UniformRandomProducer(double, double, long) - Constructor for class org.apache.ignite.ml.util.generators.primitives.scalar.UniformRandomProducer

Creates an instance of UniformRandomProducer.

unitialized() - Static method in interface org.apache.ignite.ml.environment.deploy.DeployingContext

UNKNOWN_STORAGE_MODE - Static variable in interface org.apache.ignite.ml.math.StorageConstants

Storage mode is unknown.

UnknownCategorialFeatureValue - Exception in org.apache.ignite.ml.math.exceptions.preprocessing

Indicates an unknown categorial feature value for Encoder.

UnknownCategorialFeatureValue(String) - Constructor for exception org.apache.ignite.ml.math.exceptions.preprocessing.UnknownCategorialFeatureValue

UnknownClassLabelException - Exception in org.apache.ignite.ml.selection.scoring.metric.exceptions

Indicates an unknown class label for metric calculator.

UnknownClassLabelException(double, double, double) - Constructor for exception org.apache.ignite.ml.selection.scoring.metric.exceptions.UnknownClassLabelException

unlabeled() - Method in interface org.apache.ignite.ml.util.generators.DataStreamGenerator

unsafeCoerce(DatasetTrainer<? extends M, L>) - Static method in class org.apache.ignite.ml.composition.CompositionUtils

Perform blurring of model type of given trainer to IgniteModel<I, O>, where I, O are input and output types of original model.

unsafeGet() - Method in interface org.apache.ignite.ml.environment.parallelism.Promise

Await result of Future and return it.

unsafeSimplyTyped() - Method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Performs coersion of this trainer to DatasetTrainer<IgniteModel<I, O2>, L>.

UnsupportedOperationException - Exception in org.apache.ignite.ml.math.exceptions

Indicate that a specific operation is not supported by the underlying implementation.

UnsupportedOperationException(String) - Constructor for exception org.apache.ignite.ml.math.exceptions.UnsupportedOperationException

UnsupportedOperationException() - Constructor for exception org.apache.ignite.ml.math.exceptions.UnsupportedOperationException

update(BaggedModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(GDBTrainer.GDBModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(IgniteModel<I, List<O>>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(ModelsSequentialComposition<I, O1, O2>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(StackedModel<IS, IA, O, AM>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(M1, NesterovParameterUpdate) - Method in class org.apache.ignite.ml.optimization.updatecalculators.NesterovUpdateCalculator

Update given obj with this parameters.

update(M1, P) - Method in interface org.apache.ignite.ml.optimization.updatecalculators.ParameterUpdateCalculator

Update given obj with this parameters.

update(M1, RPropParameterUpdate) - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropUpdateCalculator

Update given obj with this parameters.

update(M1, SimpleGDParameterUpdate) - Method in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Update given obj with this parameters.

update(M, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

update(M, Ignite, IgniteCache<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Gets state of model in arguments, update in according to new data and return new model.

update(M, Ignite, IgniteCache<K, V>, IgniteBiPredicate<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Gets state of model in arguments, update in according to new data and return new model.

update(M, Map<K, V>, int, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Gets state of model in arguments, update in according to new data and return new model.

update(M, Map<K, V>, IgniteBiPredicate<K, V>, int, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Gets state of model in arguments, update in according to new data and return new model.

update(GDBTrainer.GDBModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.tree.boosting.GDBOnTreesLearningStrategy

Gets state of model in arguments, compare it with training parameters of trainer and if they are fit then trainer updates model in according to new data and return new model.

updateModel(GmmModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Trains new model taken previous one as a first approximation.

updateModel(KMeansModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Trains new model taken previous one as a first approximation.

updateModel(BaggedModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(M) and DatasetTrainer.updateModel(M, org.apache.ignite.ml.dataset.DatasetBuilder<K, V>, org.apache.ignite.ml.preprocessing.Preprocessor<K, V>) in this class we explicitly override update method.

updateModel(ModelsComposition, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Trains new model taken previous one as a first approximation.

updateModel(IgniteModel<I, List<O>>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.parallel.TrainersParallelComposition

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

updateModel(ModelsSequentialComposition<I, O1, O2>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.combinators.sequential.TrainersSequentialComposition

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

updateModel(StackedModel<IS, IA, O, AM>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

This method is never called, instead of constructing logic of update from DatasetTrainer.isUpdateable(IgniteModel) and DatasetTrainer.updateModel(IgniteModel, DatasetBuilder, Preprocessor) in this class we explicitly override update method.

updateModel(ANNClassificationModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Trains new model taken previous one as a first approximation.

updateModel(M, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Trains new model taken previous one as a first approximation.

updateModel(MultiClassModel<M>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.multiclass.OneVsRestTrainer

Trains new model taken previous one as a first approximation.

updateModel(DiscreteNaiveBayesModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Trains new model taken previous one as a first approximation.

updateModel(GaussianNaiveBayesModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Trains new model taken previous one as a first approximation.

updateModel(MultilayerPerceptron, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Trains new model taken previous one as a first approximation.

updateModel(LinearRegressionModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionLSQRTrainer

Trains new model taken previous one as a first approximation.

updateModel(LinearRegressionModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Trains new model taken previous one as a first approximation.

updateModel(LogisticRegressionModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Trains new model taken previous one as a first approximation.

updateModel(SVMLinearClassificationModel, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Trains new model taken previous one as a first approximation.

updateModel(AdaptableDatasetModel<I, O, IW, OW, M>, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Trains new model taken previous one as a first approximation.

updateModel(M, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Trains new model taken previous one as a first approximation.

updateModel(DecisionTreeNode, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.tree.DecisionTree

Trains new model based on dataset because there is no valid approach to update decision trees.

updateModel(ModelsComposition, DatasetBuilder<K, V>, Preprocessor<K, V>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Trains new model taken previous one as a first approximation.

updateModifictaionTs(long) - Method in interface org.apache.ignite.ml.inference.storage.model.FileOrDirectory

Create new instance of filesystem object with modified timestamp.

updateModifictaionTs() - Method in interface org.apache.ignite.ml.inference.storage.model.FileOrDirectory

Create new instance of filesystem object with current timestamp.

updatesMask - Variable in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Updates mask (values by which updateCache is multiplied).

updatesMask() - Method in class org.apache.ignite.ml.optimization.updatecalculators.RPropParameterUpdate

Get updates mask (values by which updateCache is multiplied).

UpdatesStrategy<M,U extends Serializable> - Class in org.apache.ignite.ml.nn

Class encapsulating update strategies for group trainers based on updates.

UpdatesStrategy(ParameterUpdateCalculator<M, U>, IgniteFunction<List<U>, U>, IgniteFunction<List<U>, U>) - Constructor for class org.apache.ignite.ml.nn.UpdatesStrategy

Construct instance of this class with given parameters.

UpstreamEntry<K,V> - Class in org.apache.ignite.ml.dataset

Entry of the upstream.

UpstreamEntry(K, V) - Constructor for class org.apache.ignite.ml.dataset.UpstreamEntry

Constructs a new instance of upstream entry.

UpstreamTransformer - Interface in org.apache.ignite.ml.dataset

Interface of transformer of upstream.

UpstreamTransformerBuilder - Interface in org.apache.ignite.ml.dataset

Builder of UpstreamTransformer.

useIdx - Variable in class org.apache.ignite.ml.tree.impurity.ImpurityMeasureCalculator

Use index structure instead of using sorting while learning.

userClass() - Method in interface org.apache.ignite.ml.environment.deploy.DeployingContext

userClass() - Method in class org.apache.ignite.ml.environment.deploy.DeployingContextImpl

usingIdx - Variable in class org.apache.ignite.ml.tree.DecisionTree

Use index structure instead of using sorting while learning.

Utils - Class in org.apache.ignite.ml.util

Class with various utility methods.

Utils() - Constructor for class org.apache.ignite.ml.util.Utils

V

validateNewNode(ClusterNode) - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

valueOf(String) - Static method in enum org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.LabelCoordinate

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.environment.logging.MLLogger.VerboseLevel

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.environment.parallelism.ParallelismStrategy.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.genetic.parameter.GAGridConstants.SELECTION_METHOD

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor.Method

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.knn.utils.indices.SpatialIndexType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.preprocessing.encoding.EncoderSortingStrategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.preprocessing.encoding.EncoderType

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.preprocessing.imputing.ImputingStrategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.tree.randomforest.data.TreeNode.Type

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.util.genetic.CrossoverStrategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.util.genetic.SelectionStrategy

Returns the enum constant of this type with the specified name.

valueOf(String) - Static method in enum org.apache.ignite.ml.util.MLSandboxDatasets

Returns the enum constant of this type with the specified name.

values() - Static method in enum org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.LabelCoordinate

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.environment.logging.MLLogger.VerboseLevel

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.environment.parallelism.ParallelismStrategy.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.genetic.parameter.GAGridConstants.SELECTION_METHOD

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.inference.storage.model.thinclient.ModelStorateThinClientProcessor.Method

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.knn.utils.indices.SpatialIndexType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.preprocessing.encoding.EncoderSortingStrategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.preprocessing.encoding.EncoderType

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.preprocessing.imputing.ImputingStrategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.tree.randomforest.data.TreeNode.Type

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.util.genetic.CrossoverStrategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.util.genetic.SelectionStrategy

Returns an array containing the constants of this enum type, in the order they are declared.

values() - Static method in enum org.apache.ignite.ml.util.MLSandboxDatasets

Returns an array containing the constants of this enum type, in the order they are declared.

valuesByFrequency() - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Gets the array of maps of frequencies by value in partition for each feature in the dataset.

variance() - Method in class org.apache.ignite.ml.tree.randomforest.data.statistics.NormalDistributionStatistics

vec2Num(Vector) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Turn Vector into number by looking at index of maximal element in vector.

Vector - Interface in org.apache.ignite.ml.math.primitives.vector

A vector interface.

vector - Variable in class org.apache.ignite.ml.structures.DatasetRow

Vector.

vector() - Method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.VectorWithDistributionId

Vector.Element - Interface in org.apache.ignite.ml.math.primitives.vector

Holder for vector's element.

VectorGenerator - Interface in org.apache.ignite.ml.util.generators.primitives.vector

Basic interface for pseudorandom vectors generators.

VectorGeneratorPrimitives - Class in org.apache.ignite.ml.util.generators.primitives.vector

Collection of predefined vector generators.

VectorGeneratorPrimitives() - Constructor for class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

VectorGeneratorsFamily - Class in org.apache.ignite.ml.util.generators.primitives.vector

Represents a distribution family of district vector generators.

VectorGeneratorsFamily.Builder - Class in org.apache.ignite.ml.util.generators.primitives.vector

Helper for distribution family building.

VectorGeneratorsFamily.VectorWithDistributionId - Class in org.apache.ignite.ml.util.generators.primitives.vector

Container for vector and distribution id.

vectorize(int) - Method in interface org.apache.ignite.ml.util.generators.primitives.scalar.RandomProducer

Create VectorGenerator with vectors having feature values generated by random producer.

vectorize(RandomProducer...) - Static method in interface org.apache.ignite.ml.util.generators.primitives.scalar.RandomProducer

Creates VectorGenerator with vectors having feature values in according to preudorandom producers.

VectorizedViewMatrix - Class in org.apache.ignite.ml.math.primitives.vector.impl

Row or column vector view off the matrix.

VectorizedViewMatrix() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

VectorizedViewMatrix(Matrix, int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

VectorizedViewMatrixStorage - Class in org.apache.ignite.ml.math.primitives.vector.storage

Row, column or diagonal vector-based view of the matrix

VectorizedViewMatrixStorage() - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

VectorizedViewMatrixStorage(Matrix, int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

Vectorizer<K,V,C extends Serializable,L> - Class in org.apache.ignite.ml.dataset.feature.extractor

Class for extracting labeled vectors from upstream.

Vectorizer(C...) - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Creates an instance of Vectorizer.

Vectorizer.LabelCoordinate - Enum in org.apache.ignite.ml.dataset.feature.extractor

Shotrcuts for coordinates in feature vector.

Vectorizer.VectorizerAdapter<K,V,C extends Serializable,L> - Class in org.apache.ignite.ml.dataset.feature.extractor

Utility class for convenient overridings.

VectorizerAdapter() - Constructor for class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.VectorizerAdapter

VectorStorage - Interface in org.apache.ignite.ml.math.primitives.vector

Data storage support for Vector.

VectorUtils - Class in org.apache.ignite.ml.math.primitives.vector

Some utils for Vector.

VectorUtils() - Constructor for class org.apache.ignite.ml.math.primitives.vector.VectorUtils

VectorView - Class in org.apache.ignite.ml.math.primitives.vector.impl

Implements the partial view into the parent Vector.

VectorView() - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

Constructor for Externalizable interface.

VectorView(Vector, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

VectorView(VectorStorage, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.impl.VectorView

VectorViewStorage - Class in org.apache.ignite.ml.math.primitives.vector.storage

VectorStorage implementation that delegates to parent matrix.

VectorViewStorage() - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

VectorViewStorage(VectorStorage, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

VectorWithDistributionId(Vector, int) - Constructor for class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorsFamily.VectorWithDistributionId

Creates an instance of VectorWithDistributionId.

version() - Method in class org.apache.ignite.ml.util.plugin.MLPluginProvider

viewColumn(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new view into matrix column .

viewColumn(int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new view into matrix column .

viewDiagonal() - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new view into matrix diagonal.

viewDiagonal() - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new view into matrix diagonal.

ViewMatrix - Class in org.apache.ignite.ml.math.primitives.matrix.impl

Implements the rectangular view into the parent Matrix.

ViewMatrix() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

Constructor for Externalizable interface.

ViewMatrix(Matrix, int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

ViewMatrix(MatrixStorage, int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.impl.ViewMatrix

ViewMatrixStorage - Class in org.apache.ignite.ml.math.primitives.matrix.storage

MatrixStorage implementation that delegates to parent matrix.

ViewMatrixStorage() - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

ViewMatrixStorage(MatrixStorage, int, int, int, int) - Constructor for class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

viewPart(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

viewPart(int, int) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

viewPart(int, int) - Method in interface org.apache.ignite.ml.math.primitives.vector.Vector

viewRow(int) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

Creates new view into matrix row.

viewRow(int) - Method in interface org.apache.ignite.ml.math.primitives.matrix.Matrix

Creates new view into matrix row.

W

weight(int, int, int) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get the weight of neuron with given index in previous layer to neuron with given index in given layer.

weighted - Variable in class org.apache.ignite.ml.knn.ann.KNNModelFormat

Weighted or not.

weighted - Variable in class org.apache.ignite.ml.knn.KNNModel

Weighted or not.

weighted - Variable in class org.apache.ignite.ml.knn.KNNTrainer

Weighted or not.

weighted - Variable in class org.apache.ignite.ml.knn.NNClassificationModel

kNN strategy.

WeightedPredictionsAggregator - Class in org.apache.ignite.ml.composition.predictionsaggregator

Predictions aggregator returning weighted plus of predictions.

WeightedPredictionsAggregator(double[]) - Constructor for class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

Constructs WeightedPredictionsAggregator instance.

WeightedPredictionsAggregator(double[], double) - Constructor for class org.apache.ignite.ml.composition.predictionsaggregator.WeightedPredictionsAggregator

Constructs WeightedPredictionsAggregator instance.

weights - Variable in class org.apache.ignite.ml.nn.MLPLayer

Weights matrix.

weights(int) - Method in class org.apache.ignite.ml.nn.MultilayerPerceptron

Get weights of layer with given index.

weights() - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Gets the weights.

weights() - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Gets the weights.

withAddedLayer(int, boolean, IgniteDifferentiableDoubleToDoubleFunction) - Method in class org.apache.ignite.ml.nn.architecture.MLPArchitecture

Constructs new MLP architecture with new layer added on top of all this architecture layers.

withAggregatorInputMerger(IgniteBinaryOperator<I>) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Specify binary operator used to merge submodels outputs to one.

withAggregatorInputMerger(IgniteBinaryOperator<IA>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Specify binary operator used to merge submodels outputs to one.

withAggregatorInputMerger(IgniteBinaryOperator<Vector>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Specify binary operator used to merge submodels outputs to one.

withAggregatorTrainer(DatasetTrainer<AM, L>) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Specify aggregator trainer.

withAggregatorTrainer(DatasetTrainer<AM, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Specify aggregator trainer.

withAggregatorTrainer(DatasetTrainer<AM, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Specify aggregator trainer.

withAmountOfClusters(int) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Set up the amount of clusters.

withAmountOfEliteChromosomes(int) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withAmountOfFolds(int) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withAmountOfGenerations(int) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withAmountOfIterations(int) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Set up the amount of outer iterations of SCDA algorithm.

withAmountOfLocIterations(int) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Set up the amount of local iterations of SCDA algorithm.

withAmountOfParts(int) - Method in class org.apache.ignite.ml.selection.cv.DebugCrossValidation

withAmountOfTrees(int) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withArchSupplier(IgniteFunction<Dataset<EmptyContext, SimpleLabeledDatasetData>, MLPArchitecture>) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the multilayer perceptron architecture supplier that defines layers and activators.

withBaseModelTrainerBuilder(IgniteSupplier<DatasetTrainer<? extends IgniteModel<Vector, Double>, Double>>) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets base model builder.

withBatchSize(int) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the batch size (per every partition).

withBatchSize(int) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up batch size parameter.

withBatchSize(int) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Set up the batchSize parameter.

withBatchSize(double) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Set up the batchSize parameter.

withCategoryFrequencies(Map<String, Integer>[]) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderPartitionData

Sets the array of maps of frequencies by value in partition for each feature in the dataset.

withCheckConvergenceStgyFactory(ConvergenceCheckerFactory) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets CheckConvergenceStgyFactory.

withCheckConvergenceStgyFactory(ConvergenceCheckerFactory) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Sets CheckConvergenceStgyFactory.

withCntOfIterations(int) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets count of iterations.

withCompositionWeights(double[]) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets composition weights vector.

withConvertedLabels(IgniteFunction<L1, L>) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Creates DatasetTrainer with same training logic, but able to accept labels of given new type of labels.

withConvertedLabels(IgniteFunction<L1, L>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Creates DatasetTrainer with same training logic, but able to accept labels of given new type of labels.

withConvertedLabels(IgniteFunction<L1, L>) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Creates DatasetTrainer with same training logic, but able to accept labels of given new type of labels.

withCounts(int[]) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Sets the array of amounts of values in partition for each feature in the dataset.

withCrossingoverProbability(double) - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

withCrossingoverProbability(double) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withCrossoverStgy(CrossoverStrategy) - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

withCrossoverStgy(CrossoverStrategy) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withDatasetMapping(DatasetMapping<L, L>) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Specify DatasetMapping which will be applied to dataset before fitting and updating.

withDataTtl(long) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specify partition data time-to-live in seconds (-1 for an infinite lifetime).

withDataTtl(long) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify partition data time-to-live in seconds (-1 for an infinite lifetime).

withDataTtl(long) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Sets up dataTtl parameter.

withDefaultGradStepSize(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets default gradient step size.

withDistance(DistanceMeasure) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Set up the distance.

withDistance(DistanceMeasure) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Set up the distance.

withDistanceMeasure(DistanceMeasure) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Sets up distanceMeasure parameter.

withDistanceMeasure(DistanceMeasure) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Set up parameter of the NN model.

withEnablingROCAUC(boolean) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

withEncodedFeature(int) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

Add the index of encoded feature.

withEncodedFeatures(Set<Integer>) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

Sets the indices of features which should be encoded.

withEncoderIndexingStrategy(EncoderSortingStrategy) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

Sets the encoder indexing strategy.

withEncoderType(EncoderType) - Method in class org.apache.ignite.ml.preprocessing.encoding.EncoderTrainer

Sets the encoder preprocessor type.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.bagging.BaggedTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.boosting.GDBBinaryClassifierTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets learning environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.boosting.GDBRegressionTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.boosting.GDBTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.trainers.DatasetTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.DecisionTree

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer

Changes learning Environment.

withEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestClassifierTrainer

Changes learning Environment.

withEps(double) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets min divergence beween iterations.

withEpsilon(double) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Set up the epsilon.

withEpsilon(double) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Set up the epsilon.

withEquiprobableClasses() - Method in class org.apache.ignite.ml.naivebayes.discrete.DiscreteNaiveBayesTrainer

Sets equal probability for all classes.

withEquiprobableClasses() - Method in class org.apache.ignite.ml.naivebayes.gaussian.GaussianNaiveBayesTrainer

Sets equal probability for all classes.

withExternalLabelToInternal(IgniteFunction<Double, Double>) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets external to internal label representation mapping.

withFeature(String, BinaryObjectVectorizer.Mapping) - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.BinaryObjectVectorizer

Sets values mapping for feature.

withFeaturesCountSelectionStrgy(Function<List<FeatureMeta>, Integer>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withFilter(IgniteBiPredicate<K, V>) - Method in interface org.apache.ignite.ml.dataset.DatasetBuilder

Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

withFilter(IgniteBiPredicate<K, V>) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

withFilter(IgniteBiPredicate<K, V>) - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Returns new instance of DatasetBuilder using conjunction of internal filter and filterToAdd.

withFilter(IgniteBiPredicate<K, V>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withFitnessFunction(Function<Chromosome, Double>) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

Sets the custom fitness function.

withIdxType(SpatialIndexType) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Sets up idxType parameter.

withIgnite(Ignite) - Method in class org.apache.ignite.ml.selection.cv.CrossValidation

withImputingStrategy(ImputingStrategy) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerTrainer

Sets the imputing strategy.

withInitialCountOfComponents(int) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets numberOfComponents.

withInitialMeans(List<Vector>) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets initial means.

withInnerModel(M1) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetModel

Create new instance of this class with changed inner model.

withIntercept(double) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Set up the intercept.

withIntercept(double) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Set up the intercept.

withInternalMdl(IgniteModel<Vector, Double>) - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

withK(int) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Set up the amount of clusters.

withK(int) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Sets up k parameter (number of neighbours).

withK(int) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Set up parameter of the NN model.

withK(int) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up k parameter (number of rows/cols in matrices after factorization).

withKeepBinary(boolean) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Add keepBinary policy.

withLambda(double) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Set up the regularization parameter.

withLearningEnvironmentBuilder(LearningEnvironmentBuilder) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up learning environment builder.

withLearningRate(double) - Method in class org.apache.ignite.ml.optimization.updatecalculators.SimpleGDUpdateCalculator

Create new instance of this class with same parameters as this one, but with new learning rate.

withLearningRate(double) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up learning rate parameter.

withLocIterations(int) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the maximal number of local iterations before synchronization.

withLocIterations(int) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Set up the amount of local iterations of SGD algorithm.

withLocIterations(double) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Set up the amount of local iterations of SGD algorithm.

withLoggingFactory(T) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify logging factory.

withLoggingFactoryDependency(IgniteFunction<Integer, MLLogger.Factory>) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specify dependency (partition -> logging factory).

withLoggingFactoryDependency(IgniteFunction<Integer, MLLogger.Factory>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify dependency (partition -> logging factory).

withLoss(IgniteFunction<Vector, IgniteDifferentiableVectorToDoubleFunction>) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the loss function to be minimized during the training.

withLossGradient(Loss) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Loss function.

withMapper(UniformMapper<K, V>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withMaxCountIterations(int) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets max count of iterations

withMaxCountOfClusters(int) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets maximum number of clusters in GMM.

withMaxCountOfInitTries(int) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets MaxCountOfInitTries parameter.

withMaxDeep(Double) - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Set up the max deep of decision tree.

withMaxDepth(int) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withMaxIterations(int) - Method in class org.apache.ignite.ml.clustering.kmeans.KMeansTrainer

Set up the max number of iterations before convergence.

withMaxIterations(int) - Method in class org.apache.ignite.ml.knn.ann.ANNClassificationTrainer

Set up the max number of iterations before convergence.

withMaxIterations(int) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the maximal number of iterations before the training will be stopped.

withMaxIterations(int) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up max iterations parameter.

withMaxIterations(int) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Set up the max amount of iterations before convergence.

withMaxIterations(double) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Set up the max amount of iterations before convergence.

withMaxLikelihoodDivergence(double) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets maximum divergence between maximum of likelihood of vector in dataset and other for anomalies identification.

withMaxTries(int) - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Set up the max number of tries to stop the hyperparameter search.

withMeanLabelValue(double) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets mean label value.

withMetric(Metric<L>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withMetric(Function<M, Double>) - Method in class org.apache.ignite.ml.selection.scoring.metric.AbstractMetrics

withMinClusterProbability(double) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets minimum requred probability for cluster.

withMinElementsForNewCluster(int) - Method in class org.apache.ignite.ml.clustering.gmm.GmmTrainer

Sets minimum required anomalies in terms of maxLikelihoodDivergence for creating new cluster.

withMinImpurityDecrease(Double) - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Set up the min impurity decrease of decision tree.

withMinImpurityDelta(double) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withMinMdlImprovement(double) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up minModelImprovement parameter (minimal improvement of the model to continue training).

withMutationOperator(BiFunction<Integer, Double, Double>) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withMutationProbability(double) - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

withMutationProbability(double) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withNegativeClsLb(double) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

withNegativeClsLb(double) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Set the negative label.

withNodesToLearnSelectionStrgy(Function<Queue<TreeNode>, List<TreeNode>>) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

Sets strategy for selection nodes from learning queue in each iteration.

withOriginalFeaturesDropped() - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Drop original features during training and inference.

withOriginalFeaturesDropped() - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Drop original features during training and inference.

withOriginalFeaturesDropped() - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Drop original features during training and inference.

withOriginalFeaturesKept(IgniteFunction<I, I>) - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Keep original features during training and propagate submodels input to aggregator during inference using given function.

withOriginalFeaturesKept() - Method in class org.apache.ignite.ml.composition.stacking.SimpleStackedDatasetTrainer

Keep original features using IgniteFunction.identity() as submodelInput2AggregatingInputConverter.

withOriginalFeaturesKept(IgniteFunction<IS, IA>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Keep original features during training and propagate submodels input to aggregator during inference using given function.

withOriginalFeaturesKept() - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Keep original features using IgniteFunction.identity() as submodelInput2AggregatingInputConverter.

withOriginalFeaturesKept(IgniteFunction<Vector, Vector>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Keep original features during training and propagate submodels input to aggregator during inference using given function.

withP(double) - Method in class org.apache.ignite.ml.preprocessing.normalization.NormalizationTrainer

Sets the p parameter value.

withParallelismStrategy(T) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specifies Parallelism Strategy for LearningEnvironment.

withParallelismStrategyDependency(IgniteFunction<Integer, ParallelismStrategy>) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specifies dependency (partition -> Parallelism Strategy for LearningEnvironment).

withParallelismStrategyDependency(IgniteFunction<Integer, ParallelismStrategy>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specifies dependency (partition -> Parallelism Strategy for LearningEnvironment).

withParallelismStrategyType(ParallelismStrategy.Type) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specifies Parallelism Strategy Type for LearningEnvironment.

withParallelismStrategyTypeDependency(IgniteFunction<Integer, ParallelismStrategy.Type>) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specifies dependency (partition -> Parallelism Strategy Type for LearningEnvironment).

withParallelismStrategyTypeDependency(IgniteFunction<Integer, ParallelismStrategy.Type>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specifies dependency (partition -> Parallelism Strategy Type for LearningEnvironment).

withParameterSearchStrategy(HyperParameterTuningStrategy) - Method in class org.apache.ignite.ml.selection.paramgrid.ParamGrid

Set up the hyperparameter searching strategy.

withParamGrid(ParamGrid) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withPipeline(Pipeline<K, V, Integer, Double>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withPopulationSize(int) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withPositiveClsLb(double) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.BinaryClassificationMetrics

withPositiveClsLb(double) - Method in class org.apache.ignite.ml.selection.scoring.metric.classification.ROCAUC

Set the positive label.

withPreprocessor(Preprocessor<K, V>) - Method in class org.apache.ignite.ml.pipeline.PipelineMdl

withPreprocessor(Preprocessor<K, V>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withRandom(Random) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify random numbers generator for learning environment.

withRandomDependency(IgniteFunction<Integer, Random>) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specify dependency (partition -> random numbers generator).

withRandomDependency(IgniteFunction<Integer, Random>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify dependency (partition -> random numbers generator).

withRawLabels(boolean) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Set up the output label format.

withRawLabels(boolean) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Set up the output label format.

withRegularizer(double) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up regularization parameter.

withRetriesNumber(int) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Sets number of retries. 15 * 60 by default.

withRNGSeed(long) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify seed for random number generator.

withRNGSeedDependency(IgniteFunction<Integer, Long>) - Method in class org.apache.ignite.ml.environment.DefaultLearningEnvironmentBuilder

Specify dependency (partition -> seed for random number generator).

withRNGSeedDependency(IgniteFunction<Integer, Long>) - Method in interface org.apache.ignite.ml.environment.LearningEnvironmentBuilder

Specify dependency (partition -> seed for random number generator).

withSampleSize(long) - Method in class org.apache.ignite.ml.composition.boosting.GDBLearningStrategy

Sets sample size.

withSatisfactoryFitness(double) - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Set up the satisfactory fitness to stop the hyperparameter search.

withSeed(long) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the multilayer perceptron model initializer.

withSeed(long) - Method in class org.apache.ignite.ml.regressions.linear.LinearRegressionSGDTrainer

Set up the random seed parameter.

withSeed(long) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Set up the random seed parameter.

withSeed(long) - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

Set up the seed number.

withSeed(long) - Method in class org.apache.ignite.ml.selection.paramgrid.RandomStrategy

Set up the seed number.

withSeed(long) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationTrainer

Set up the seed.

withSeed(long) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withSelectionStgy(SelectionStrategy) - Method in class org.apache.ignite.ml.selection.paramgrid.EvolutionOptimizationStrategy

withSelectionStgy(SelectionStrategy) - Method in class org.apache.ignite.ml.util.genetic.GeneticAlgorithm

withSubmodelOutput2VectorConverter(IgniteFunction<IA, Vector>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Set function used for conversion of submodel output to Vector.

withSubmodelOutput2VectorConverter(IgniteFunction<Vector, Vector>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Set function used for conversion of submodel output to Vector.

withSubSampleSize(double) - Method in class org.apache.ignite.ml.tree.randomforest.RandomForestTrainer

withSums(double[]) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Sets the array of sums of values in partition for each feature in the dataset.

withThreshold(double) - Method in class org.apache.ignite.ml.preprocessing.binarization.BinarizationTrainer

Set the threshold parameter value.

withThreshold(double) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Set up the threshold.

withThreshold(double) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Set up the threshold.

withTrainer(DatasetTrainer<M, L>) - Method in class org.apache.ignite.ml.selection.cv.AbstractCrossValidation

withTrainerEnvironment(LearningEnvironment) - Method in class org.apache.ignite.ml.recommendation.RecommendationTrainer

Set up trainer learning environment.

withUpdatesStgy(UpdatesStrategy<? super MultilayerPerceptron, P>) - Method in class org.apache.ignite.ml.nn.MLPTrainer

Set up the update strategy that defines how to update model parameters during the training.

withUpdatesStgy(UpdatesStrategy) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionSGDTrainer

Set up the regularization parameter.

withUpstreamCache(IgniteCache<K, V>) - Method in class org.apache.ignite.ml.selection.cv.CrossValidation

withUpstreamMap(Map<K, V>) - Method in class org.apache.ignite.ml.selection.cv.DebugCrossValidation

withUpstreamTransformer(UpstreamTransformerBuilder) - Method in interface org.apache.ignite.ml.dataset.DatasetBuilder

Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

withUpstreamTransformer(UpstreamTransformerBuilder) - Method in class org.apache.ignite.ml.dataset.impl.cache.CacheBasedDatasetBuilder

Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

withUpstreamTransformer(UpstreamTransformerBuilder) - Method in class org.apache.ignite.ml.dataset.impl.local.LocalDatasetBuilder

Returns new instance of DatasetBuilder with new UpstreamTransformerBuilder added to chain of upstream transformer builders.

withUpstreamTransformerBuilder(UpstreamTransformerBuilder) - Method in class org.apache.ignite.ml.trainers.AdaptableDatasetTrainer

Specify which UpstreamTransformerBuilder will be used.

withUseIndex(boolean) - Method in class org.apache.ignite.ml.tree.DecisionTreeClassificationTrainer

Sets useIndex parameter and returns trainer instance.

withUsingIdx(boolean) - Method in class org.apache.ignite.ml.tree.boosting.GDBBinaryClassifierOnTreesTrainer

Set useIndex parameter and returns trainer instance.

withUsingIdx(boolean) - Method in class org.apache.ignite.ml.tree.boosting.GDBRegressionOnTreesTrainer

Set useIndex parameter and returns trainer instance.

withUsingIdx(boolean) - Method in class org.apache.ignite.ml.tree.DecisionTreeRegressionTrainer

Sets usingIdx parameter and returns trainer instance.

withValuesByFrequency(Map<Double, Integer>[]) - Method in class org.apache.ignite.ml.preprocessing.imputing.ImputerPartitionData

Sets the array of maps of frequencies by value in partition for each feature in the dataset.

withVector2SubmodelInputConverter(IgniteFunction<Vector, IS>) - Method in class org.apache.ignite.ml.composition.stacking.StackedDatasetTrainer

Set function used for conversion of Vector to submodel input.

withVector2SubmodelInputConverter(IgniteFunction<Vector, Vector>) - Method in class org.apache.ignite.ml.composition.stacking.StackedVectorDatasetTrainer

Set function used for conversion of Vector to submodel input.

withWeighted(boolean) - Method in class org.apache.ignite.ml.knn.KNNTrainer

Sets up weighted parameter.

withWeighted(boolean) - Method in class org.apache.ignite.ml.knn.NNClassificationModel

Sets up weighted parameter.

withWeights(Vector) - Method in class org.apache.ignite.ml.regressions.logistic.LogisticRegressionModel

Set up the weights.

withWeights(Vector) - Method in class org.apache.ignite.ml.svm.SVMLinearClassificationModel

Set up the weights.

wrap(IgniteFunction<Dataset<C, D>, I>) - Method in interface org.apache.ignite.ml.dataset.Dataset

Wraps this dataset into the specified wrapper to introduce new functionality based on compute and computeWithCtx methods.

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.dataset.impl.bootstrapping.BootstrappedVector

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.distances.EuclideanDistance

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.distances.HammingDistance

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.distances.ManhattanDistance

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.matrix.AbstractMatrix

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.DenseMatrixStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.SparseMatrixStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.matrix.storage.ViewMatrixStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.AbstractVector

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingNamedVector

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.DelegatingVector

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.impl.VectorizedViewMatrix

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.DenseVectorStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.SparseVectorStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorizedViewMatrixStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.math.primitives.vector.storage.VectorViewStorage

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.structures.Dataset

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.structures.DatasetRow

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.structures.FeatureMetadata

writeExternal(ObjectOutput) - Method in class org.apache.ignite.ml.structures.LabeledVector

Z

zero() - Method in class org.apache.ignite.ml.dataset.feature.extractor.ExtractionUtils.DefaultLabelVectorizer

Returns default label value for unlabeled data.

zero() - Method in class org.apache.ignite.ml.dataset.feature.extractor.impl.LabeledDummyVectorizer

Returns default label value for unlabeled data.

zero() - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer.VectorizerAdapter

Returns default label value for unlabeled data.

zero() - Method in class org.apache.ignite.ml.dataset.feature.extractor.Vectorizer

Returns default label value for unlabeled data.

zero(int) - Static method in class org.apache.ignite.ml.util.generators.primitives.vector.VectorGeneratorPrimitives

zeroes(int) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Create new

zeroesLike(Vector) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Create new vector like given vector initialized by zeroes.

zipFoldByColumns(Matrix, Matrix, IgniteBiFunction<Vector, Vector, Double>) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Zips two matrices by column-by-column with specified function.

zipFoldByRows(Matrix, Matrix, IgniteBiFunction<Vector, Vector, Double>) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Zips two matrices by row-by-row with specified function.

zipWith(Vector, Vector, IgniteBiFunction<Double, Double, Double>) - Static method in class org.apache.ignite.ml.math.primitives.vector.VectorUtils

Zip two vectors with given binary function (i.e. apply binary function to both vector elementwise and construct vector from results).

zipWith(Vector, Vector, IgniteTriFunction<Double, Double, Integer, Double>) - Static method in class org.apache.ignite.ml.math.util.MatrixUtil

Zip two vectors with given tri-function taking as third argument position in vector (i.e. apply binary function to both vector elementwise and construct vector from results).

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